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Abstract

We evaluated the performance of a consumer multi-sensory wristband (Fitbit Charge 2™), against polysomnography (PSG) in measuring sleep/wake state and sleep stage composition in healthy adults. In-lab PSG and Fitbit Charge 2™ data were obtained from a single overnight recording at the SRI Human Sleep Research Laboratory in 44 adults (19—61 years; 26 women; 25 Caucasian). Participants were screened to be free from mental and medical conditions. Presence of sleep disorders was evaluated with clinical PSG. PSG findings indicated periodic limb movement of sleep (PLMS, > 15/h) in nine participants, who were analyzed separately from the main group (n = 35). PSG and Fitbit Charge 2™ sleep data were compared using paired t-tests, Bland–Altman plots, and epoch-by-epoch (EBE) analysis. In the main group, Fitbit Charge 2™ showed 0.96 sensitivity (accuracy to detect sleep), 0.61 specificity (accuracy to detect wake), 0.81 accuracy in detecting N1+N2 sleep (“light sleep”), 0.49 accuracy in detecting N3 sleep (“deep sleep”), and 0.74 accuracy in detecting rapid-eye-movement (REM) sleep. Fitbit Charge 2™ significantly (p < 0.05) overestimated PSG TST by 9 min, N1+N2 sleep by 34 min, and underestimated PSG SOL by 4 min and N3 sleep by 24 min. PSG and Fitbit Charge 2™ outcomes did not differ for WASO and time spent in REM sleep. No more than two participants fell outside the Bland–Altman agreement limits for all sleep measures. Fitbit Charge 2™ correctly identified 82% of PSG-defined non-REM–REM sleep cycles across the night. Similar outcomes were found for the PLMS group. Fitbit Charge 2™ shows promise in detecting sleep-wake states and sleep stage composition relative to gold standard PSG, particularly in the estimation of REM sleep, but with limitations in N3 detection. Fitbit Charge 2™ accuracy and reliability need to be further investigated in different settings (at-home, multiple nights) and in different populations in which sleep composition is known to vary (adolescents, elderly, patients with sleep disorders).

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... The latest validity studies of wrist-worn activity trackers showed clear improvements in the accuracy of the sleep analyses compared to the first studies from 2015 [29,30,44]. However, differences in the degree of accuracy can be seen depending on the brand, the sensors and algorithms used [2,45,46], with accuracies to the gold standard between moderate and good in measuring sleep phases such as TST, WASO, SOL and SE [47]. ...
... It has been found that trackers for sleep patients with sleep disorders are not yet sufficiently developed. Another study showed that the accuracy of determining deep sleep with the tracker was limited, with a medium accuracy of 0.49 [47]. Moreno-Pino et al. [48] found that the tracker recorded significant differences in all sleep stages except in REM. ...
... Moreno-Pino et al. [48] found that the tracker recorded significant differences in all sleep stages except in REM. In most studies, light sleep was overestimated and deep sleep was underestimated [12,31,47,48]. Results in the literature show a variable accuracy (moderate to good) of the wrist-worn activity trackers. ...
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Article
Two commercial multisport activity trackers (Garmin Forerunner 945 and Polar Ignite) and the accelerometer ActiGraph GT9X were evaluated in measuring vital data, sleep stages and sleep/wake patterns against polysomnography (PSG). Forty-nine adult patients with suspected sleep disorders (30 males/19 females) completed a one-night PSG sleep examination followed by a multiple sleep latency test (MSLT). Sleep parameters, time in bed (TIB), total sleep time (TST), wake after sleep onset (WASO), sleep onset latency (SOL), awake time (WASO + SOL), sleep stages (light, deep, REM sleep) and the number of sleep cycles were compared. Both commercial trackers showed high accuracy in measuring vital data (HR, HRV, SpO2, respiratory rate), r > 0.92. For TIB and TST, all three trackers showed medium to high correlation, r > 0.42. Garmin had significant overestimation of TST, with MAE of 84.63 min and MAPE of 25.32%. Polar also had an overestimation of TST, with MAE of 45.08 min and MAPE of 13.80%. ActiGraph GT9X results were inconspicuous. The trackers significantly underestimated awake times (WASO + SOL) with weak correlation, r = 0.11–0.57. The highest MAE was 50.35 min and the highest MAPE was 83.02% for WASO for Garmin and ActiGraph GT9X; Polar had the highest MAE of 21.17 min and the highest MAPE of 141.61% for SOL. Garmin showed significant deviations for sleep stages (p < 0.045), while Polar only showed significant deviations for sleep cycle (p = 0.000), r < 0.50. Garmin and Polar overestimated light sleep and underestimated deep sleep, Garmin significantly, with MAE up to 64.94 min and MAPE up to 116.50%. Both commercial trackers Garmin and Polar did not detect any daytime sleep at all during the MSLT test. The use of the multisport activity trackers for sleep analysis can only be recommended for general daily use and for research purposes. If precise data on sleep stages and parameters are required, their use is limited. The accuracy of the vital data measurement was adequate. Further studies are needed to evaluate their use for medical purposes, inside and outside of the sleep laboratory. The accelerometer ActiGraph GT9X showed overall suitable accuracy in detecting sleep/wake patterns.
... Pulse-rate based solutions rely on the same principle as heart-rate based solution (the estimation of the ANS activity) and further alleviate the heavy requirements of sleep staging, as the pulse-rate acquisition can be done with simple solutions such as, for instance: • Photoplethysmography (PPG), optical measurement of volumetric variations due to blood flow. PPG-based solutions often rely on watches [7], armbands, rings [8], and so on and can easily be combined with pulse oximetry, which may be useful in the assessment of sleep apnoea. ...
... L'ANS pilote la balance sympathique/parasympathique, ou sympathovagale. Cette dernière présente l'avantage de pouvoir être estimée à partir du rythme cardiaque [6], dont la mesure est facile: patch au torse, mesure photoplethysmographiques (PPG) [7,8], estimation par ballistocardiographie (BCG) [9], etc. De ce fait, de nombreuses alternatives à la PSG ont été proposées pour leur praticité, réduisant l'examen au simple port d'un bandeau, d'un brassard, ou encore la mise en place de l'appareil de mesure sous le matelas; outre la réduction des contraintes liées à la PSG (nombreuses électrodes), cela permet également de réaliser l'examen hors milieu médical spécialisé. La Solution Somno-Art [12] ...
Thesis
This doctoral project aims at improving an automatic sleep staging system by taking into account inter-and-intra-individual variabilities, the latter having adversary effects on the classification. We focus on the detection of Rapid-Eye Movement periods during sleep. The core of our research is transfer learning and the selection of suitable detector(s) among a set, allowing the individualisation of the analysis by the exploitation of the observed data properties. We focus on the application of kernel alignment methods, firstly through the use of kernel-target alignment, studied here in a dual way, i.e. the kernel is fixed and the criterion is optimised with respect to the sought target labels. In a second step, we introduced kernel-cross alignment, allowing to take more efficiently advantage of the information contained in the training data. The ideas developed in the framework of this work have been extended to automatically selecting one or more efficient training sets for a given test set. The contributions of this work are both methodological and algorithmic, general in scope, but also focused on the application.
... These studies depended only on movement to distinguish between sleep and wake (Fitbit "original", specificity of 20.0% [23]; Fitbit Flex, specificity of 35.0% and 36.0% [41]; Fitbit Ultra, specificity of 52.0% [18]; Fitbit Charge HR, specificity of 42.0% [42]; and Fitbit Charge 3 TM , specificity of 61.0% [43]). Previous studies have shown low specificity for former Fitbit types relative to PSG, usually less than 0.5 [44,45]. ...
... However, REM sleep estimation by FBC was accurate on average. Therefore, our findings replicate those of de Zambotti [43] and Menghini [25]. Owing to the development in transducer ability and signal processing technology, FBC has recently applied a multisensory information detection system for sleep detection. ...
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Article
Our research aims to assess the performance of a new generation of consumer activity trackers (Fitbit Charge 4TM: FBC) to measure sleep variables and sleep stage classifications in patients with chronic insomnia, compared to polysomnography (PSG) and a widely used actigraph (Actiwatch Spectrum Pro: AWS). We recruited 37 participants, all diagnosed with chronic insomnia disorder, for one night of sleep monitoring in a sleep laboratory using PSG, AWS, and FBC. Epoch-by-epoch analysis along with Bland-Altman plots was used to evaluate FBC and AWS against PSG for sleep-wake detection and sleep variables: total sleep time (TST), sleep efficiency (SE), waking after sleep onset (WASO), and sleep onset latency (SOL). FBC sleep stage classification of light sleep (LS), deep sleep (DS), and rapid eye movement (REM) was also compared to that of PSG. When compared with PSG, FBC notably underestimated DS (-41.4, p < 0.0001) and SE (-4.9%, p = 0.0016), while remarkably overestimating LS (37.7, p = 0.0012). However, the TST, WASO, and SOL assessed by FBC presented no significant difference from that assessed by PSG. Compared with PSG, AWS and FBC showed great accuracy (86.9% vs. 86.5%) and sensitivity (detecting sleep; 92.6% vs. 89.9%), but comparatively poor specificity (detecting wake; 35.7% vs. 62.2%). Both devices showed better accuracy in assessing sleep than wakefulness, with the same sensitivity but statistically different specificity. FBC supplied equivalent parameters estimation as AWS in detecting sleep variables except for SE. This research shows that FBC cannot replace PSG thoroughly in the quantification of sleep variables and classification of sleep stages in Chinese patients with chronic insomnia; however, the user-friendly and low-cost wearables do show some comparable functions. Whether FBC can serve as a substitute for actigraphy and PSG in patients with chronic insomnia needs further investigation.
... Such wearables, including the Oura ring, usually utilize accelerometer in sleep tracking (such as Fitbit Flex, Jawbone UP) [21]. They may also include heart rate, blood flow, respiratory rate, and movement sensors, with prior work highlighting their ability to accurately offer detailed information on the wearer's sleep stages [12,13]. In addition to the accelerometer, the Oura ring measures body temperature and utilizes PPG for heart rate and respiration measurements. ...
... Prior work has investigated the validity of sleep tracking devices [12,13], including validation of the Oura ring against polysomnography [13] and actigraphy [32]. This paper focuses on how tracking activity impacts the user's behaviour and how users reflect on their personal sleep data. ...
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Article
A new generation of wearable devices now enable end-users to keep track of their sleep patterns. This paper reports on a longitudinal study of 82 participants who used a state-of-the-art sleep tracking ring for an average of 65 days. We conducted interviews and questionnaires to understand changes to their lifestyle, their perceptions of the tracked information and sleep, and the overall experience of using an unobtrusive sleep tracking device. Our results indicate that such a device is suitable for long-term sleep tracking and helpful in identifying detrimental lifestyle elements that hinder sleep quality. However, tracking one's sleep can also introduce stress or physical discomfort, potentially leading to adverse outcomes. We discuss these findings in light of related work and highlight the near-term research directions that the rapid commoditisation of sleep tracking technology enables.
... Nonetheless, these findings highlight the importance of taking an individual-specific approach to sleep counselling in epilepsy management and reiterate the value of wearables in epilepsy research, given that wearable devices typically perform well at detecting sleep onset and offset. 32,33 . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. ...
... Recent studies have shown that newer Fitbit devices, which rely on multiple signals to detect sleep stages, have satisfactory performance with regards to their total sleep duration estimations and the transition from wake to sleep and sleep to wake. 32,33 However, for classification of sleep stages, wearable smartwatches are not yet suitable substitutes for polysomnography, 22,23,34 with a few exceptions in their ability to detect transitioning from certain states (deep sleep to wake state, and light sleep to REM sleep) and the likelihood of remaining in REM sleep. 34 Despite this lack of confidence in classification of sleep stages, it is still possible to detect clear individualspecific relationships between seizures and other sleep parameters, notably total sleep duration, sleep onset and offset, and oversleep and undersleep, which indirectly relate to sleep architecture and sleep quality. ...
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Preprint
Sleep duration, sleep deprivation and the sleep-wake cycle are thought to play an important role in the generation of epileptic activity and may also influence seizure risk. Hence, people diagnosed with epilepsy are commonly asked to maintain consistent sleep routines. However, emerging evidence paints a more nuanced picture of the relationship between seizures and sleep, with bidirectional effects between changes in sleep and seizure risk in addition to modulation by sleep stages and transitions between stages. We conducted a longitudinal study investigating sleep parameters and self-reported seizure occurrence in an ambulatory at-home setting using mobile and wearable monitoring. Forty-four subjects wore a Fitbit smartwatch for at least 28 days while reporting their seizure activity in a mobile app. Multiple sleep features were investigated, including duration, oversleep and undersleep, and sleep onset and offset times. Sleep features in participants with epilepsy were compared to a large (n=37921) representative population of Fitbit users, each with 28 days of data. For participants with at least 10 seizure days (n=29), sleep features were analysed for significant changes prior to seizure days. A total of 3894 reported seizures (M = 88, SD = 130) and 17078 recorded sleep nights (M = 388, SD = 351) were included in the study. Participants with epilepsy slept an average of 2 hours longer than the average sleep duration within the general population. Just 1 of 29 participants showed a significant difference in sleep duration the night before seizure days compared to seizure-free days. However, 11 of 29 subjects showed significant differences between either their sleep onset (bed) or offset (wake) times prior to seizure occurrence. In contrast to previous studies, the current study found oversleeping was associated with a 20% increased seizure risk in the following 48h (p < 0.01), likely due to nocturnal seizures driving increased sleep durations. Nocturnal seizures were associated with both significantly longer sleep durations and increased risk of a seizure occurring in the following 48h. Oversleeping only significantly contributed to increased seizure risk when participants were already in a high-risk (rather than baseline- or low-risk) state, according to their endogenous cycles of seizure likelihood. Overall, the presented results demonstrated that day-to-day changes in sleep-duration had a minimal effect on reported seizures, while bed- and wake-times were more important for identifying seizure risk the following day. Oversleeping was linked to seizure occurrence, most likely due to nocturnal seizures driving oversleep. Wearables can be utilised to identify these sleep-seizure relationships and guide clinical recommendations or improve seizure forecasting algorithms.
... Additionally, the lack of information about validity and accuracy in CSTs makes it difficult for providers to trust and use the data and information recorded and produced [6]. Recent studies have shown that newer commercial devices are performing better than earlier models [7,8]. ...
... Overall, there remain challenges for the scientific community to validate CSTs given the time-consuming nature of conducting these studies and the quick pace of change in CSTs [8]. Although CSTs are not currently approved for clinical purposes or diagnosis of any conditions, they may complement traditional PSG data. ...
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Article
This study aims to assess the perspectives and usability of different consumer sleep technologies (CSTs) that leverage artificial intelligence (AI). We answer the following research questions: (1) what are user perceptions and ideations of CSTs (phase 1), (2) what are the users’ actual experiences with CSTs (phase 2), (3) and what are the design recommendations from participants (phases 1 and 2)? In this two-phase qualitative study, we conducted focus groups and usability testing to describe user ideations of desires and experiences with different AI sleep technologies and identify ways to improve the technologies. Results showed that focus group participants prioritized comfort, actionable feedback, and ease of use. Participants desired customized suggestions about their habitual sleeping environments and were interested in CSTs+AI that could integrate with tools and CSTs they already use. Usability study participants felt CSTs+AI provided an accurate picture of the quantity and quality of sleep. Participants identified room for improvement in usability, accuracy, and design of the technologies. We conclude that CSTs can be a valuable, affordable, and convenient tool for people who have issues or concerns with sleep and want more information. They provide objective data that can be discussed with clinicians.
... Second is social sleep research [34,45,57] including the sociology of health and illness [14,68] and qualitative research [59]. And the third area is medical sleep research [7,8,47,64] including the study of chronobiology [18,33], clinical sleep medicine [6,55,62] and medical computing [4,32,51]. ...
... They also require a certain technological affinity which might exclude the elderly. Improvements in accuracy of the devices could contribute to facilitating and reducing the cost of clinical sleep research and improve global access to healthcare [18]. ...
... However, the fact that a good-accuracy model was built using the machine learning XGBoost method shows that actual societal applications can be expected (37). The accuracy of consumer-grade wearable devices has continually increased, and many validation studies have discussed methods for evaluating and utilizing the quality of such devices (38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51). Recent systematic reviews relating to the measurement accuracy of sleep and physical activity by Fitbit have shown that, although there are still areas for improvement, it is fundamentally accurate (52)(53)(54)(55). ...
... A previous study using a polysomnography showed that shortened REM sleep latency was associated with depression (62), whereas the disruption and shortening of REM sleep was associated with poor physical and mental health (63)(64)(65)(66)(67). Some Fitbit models have been reported to have favorable REM sleep estimation accuracies (46), but care should be taken with regards to the sleep-stage estimation accuracy from consumer-grade wearable devices. Smaller SRI1 was evaluated as high risk, and this is consistent with previous studies that stated that irregular daily sleep times led to poor mental and physical states (60,68,69). ...
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Article
The prevention and treatment of mental illness is a serious social issue. Prediction and intervention, however, have been difficult because of lack of objective biomarkers for mental illness. The objective of this study was to use biometric data acquired from wearable devices as well as medical examination data to build a predictive model that can contribute to the prevention of the onset of mental illness. This was an observational study of 4,612 subjects from the health database of society-managed health insurance in Japan provided by JMDC Inc. The inputs to the predictive model were 3-months of continuous wearable data and medical examinations within and near that period; the output was the presence or absence of mental illness over the following month, as defined by insurance claims data. The features relating to the wearable data were sleep, activity, and resting heart rate, measured by a consumer-grade wearable device (specifically, Fitbit). The predictive model was built using the XGBoost algorithm and presented an area-under-the-receiver-operating-characteristic curve of 0.712 (SD = 0.02, a repeated stratified group 10-fold cross validation). The top-ranking feature importance measure was wearable data, and its importance was higher than the blood-test values from medical examinations. Detailed verification of the model showed that predictions were made based on disrupted sleep rhythms, mild physical activity duration, alcohol use, and medical examination data on disrupted eating habits as risk factors. In summary, the predictive model showed useful accuracy for grouping the risk of mental illness onset, suggesting the potential of predictive detection, and preventive intervention using wearable devices. Sleep abnormalities in particular were detected as wearable data 3 months prior to mental illness onset, and the possibility of early intervention targeting the stabilization of sleep as an effective measure for mental illness onset was shown.
... Despite of their convenience for longitudinal use in daily life settings, quantified-self sleep tracking devices may have limited accuracy depending on the type of sensors used [9]. The validity of popular sleep trackers has been well-studied in both laboratories [13] and naturalistic settings [2]. Recent findings showed that the latest models achieved reasonable accuracy for TST and SE, but not for sleep stages [2,13]. ...
... The validity of popular sleep trackers has been well-studied in both laboratories [13] and naturalistic settings [2]. Recent findings showed that the latest models achieved reasonable accuracy for TST and SE, but not for sleep stages [2,13]. In addition, device accuracy was found to correlate to many factors including the demographic characteristics and the sleep structure of the users [3,21], and may also demonstrate temporal patterns [20]. ...
Chapter
Quantified-self sleep tracking devices such as Fitbit are gaining great popular in recent years. However, users often complain about the discrepancy between the data collected with sleep trackers and their subjective sleep experience, which is often attributed to the accuracy issue of the devices. In this pilot study, we aim to provide an explanation to such discrepancy from a neuroscience perspective. We investigated the associations of subjective sleep rating and Fitbit measured sleep data to cortical hemodynamics in the prefrontal cortex (PFC) during the first sleep cycle. Correlation analysis results showed that subjective sleep rating mainly correlated to the median of the concentration changes in oxyhemoglobin (ΔO2Hb) and deoxyhemoglobin (ΔHHb) in a set of channels, with positive correlation coefficients. In contrast, the sleep score computed by Fitbit mainly correlated to the mean of the ΔO2Hb and ΔHHb in a different set of channels, with negative correlation coefficients. The findings suggested that better perceived sleep quality may be positively associated to increased hemodynamics during the first sleep cycle, and the opposite may be true for objective sleep metrics such as sleep score measured by Fitbit. The result implies that users’ subjective perception of sleep and the sleep tracking devices may be capturing different dimensions of sleep. As such, improving device accuracy may help little in addressing the discrepancy between the subjective sleep experience and the objective data. The findings provided design implications for the development of future sleep tracking technologies.
... Then, the background temperature change was measured and subsequently removed from the thermal image, leaving only the temperature change caused by the expiratory airflow. To estimate the distribution and diffusion rate of the exhalation airflow from these separate expiratory airflow images, an equal-interval layer-feature detector was used to convert the respiratory airflow signal 14 . Through digital image processing, we improved the visibility of the airflow image in Fig. 1c. ...
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Article
Full-night polysomnography (PSG) is the gold standard for diagnosing obstructive sleep apnea (OSA). However, PSG requires several sensors to be attached to the patient’s body, which can interfere with sleep. Moreover, non-contact devices that utilize impulse radio ultra-wideband radar have limitations as they cannot directly measure respiratory airflow. This study aimed to detect respiratory events through infrared optical gas imaging and verify its feasibility for the diagnosis of OSA. Data collection through PSG and infrared optical gas imaging was simultaneously conducted on 50 volunteers. Respiratory airflow signal was extracted from the infrared optical gas images using an automated algorithm. We compared the respiratory parameters obtained from infrared optical gas imaging with those from PSG. All respiratory events scored from the infrared optical gas imaging were strongly correlated with those identified with standard PSG sensors. Based on a receiver operating characteristic curve, infrared optical gas imaging was deemed appropriate for the diagnosis of OSA. Infrared optical gas imaging accurately detected respiratory events during sleep; therefore, it may be employed as a screening tool for OSA.
... The Fitbit measured seven sleep parameters: sleep duration (total minutes of sleep per night), percentage of deep sleep per night, percentage of light sleep per night, the percentage of REM sleep per night, onset and offset of sleep, number of awakenings in the night, and time spent in bed. Fitbit smartwatch devices use measures such as heart rate and movement to estimate sleep quality parameters and have been validated against comparable clinical sleep monitors, yielding similar results for sleep onset and offset, sleep duration and efficiency, and sleep structure [39,40]. ...
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Article
The COVID-19 pandemic has posed unique academic, social, financial, and health-related challenges for young adults. While numerous studies have documented average increases in reported mental health issues in the general population, few have measured the magnitude of changes in mental health symptoms and sleep difficulties within individuals. Here, we measure the impact of the COVID-19 pandemic on mental health and sleep of university students pre- and mid-pandemic. Prior to the pandemic (Fall 2019), individuals (n = 23) were recruited to participate in an eight-day, comprehensive sleep study using Fitbit® actigraphy. Participants also completed detailed mental health and sleep surveys, including depression (BDI-II), anxiety (STAI), and sleep disturbance (PROMIS) surveys. One year later, these individuals repeated the study during the pandemic (Fall 2020); participants completed the original surveys and sleep study, in addition to a targeted survey on mental and sleep health due to the pandemic. Self-reported levels of anxiety, depression, and sleep disturbance, and sleep parameters, measured by actigraphy, were compared within the same individuals pre- and mid-pandemic. Self-report survey data revealed that three-quarters of participants experienced an increase in stress and anxiety due to the pandemic. In addition, intra-individual depression and anxiety symptoms increased to clinically significant levels within individuals from pre- to mid-pandemic. Over two-thirds of participants reported sleeping less, and more than half reported that their sleep health had worsened during the pandemic. Changes in sleep disturbance were positively associated with changes in depression and anxiety, reinforcing the robust relationship between poor sleep quality and mental health. Furthermore, individuals who reported greater sleep disturbance during the pandemic experienced lower relative proportions of both REM and deep sleep. The impact of the COVID-19 pandemic on university students is multi-faceted-mental health, sleep quality, and the amount of restorative sleep are negatively affected by the pandemic environment. These compounded effects exacerbate the health consequences of the pandemic and highlight a need for increased attention to the prevention and treatment of mental health disorders, particularly in vulnerable populations of young adults.
... Earlier concerns about data quality obtained from early consumer sleep trackers are continually being addressed by both improvements in measurement technology and a growing number of rigorous performance evaluation studies demonstrating high correlation (r's > 0.70) of sleep measurements using PSG and/or research actigraphy, [18][19][20][21] alongside development of a standardized testing framework. 22,23 This provides increased assurance regarding the reliability of single night sleep measurements. ...
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Article
Study Objectives To determine the minimum number of nights required to reliably estimate weekly and monthly mean sleep duration and sleep variability measures from a consumer sleep technology (CST) device (Fitbit). Methods Data comprised 107,144 nights from 1041 working adults aged 21-40y. Intraclass correlation (ICC) analyses were conducted on both weekly and monthly time windows to determine the number of nights required to achieve ICC values of 0.60 and 0.80, corresponding to “good” and “very good” reliability thresholds. These minimum numbers were then validated on data collected 1-month and 1-year later. Results Minimally, 3 and 5 nights were required to obtain “good” and “very good” mean weekly total sleep time (TST) estimates, while 5 and 10 nights were required for monthly TST estimates. For weekday-only estimates, 2 and 3 nights were sufficient for weekly time windows while 3 and 7 nights sufficed for monthly time windows. Weekend-only estimates of monthly TST required 3 and 5 nights. TST variability required 5 and 6 nights for weekly time windows, and 11 and 18 nights for monthly time windows. Weekday-only weekly variability required 4 nights for both “good” and “very good” estimates while monthly variability required 9 and 14 nights. Weekend-only estimates of monthly variability required 5 and 7 nights. Error estimates made using data collected 1-month and 1-year later with these parameters were comparable to those associated with the original dataset. Conclusion Studies should consider the metric, measurement window of interest and desired reliability threshold to decide on the minimum number of nights required to assess habitual sleep using CST devices.
... Many wearable devices have validation studies, comparing them with PSG, and many others are in process, with previous results showing similarity of the data from these devices with PSG. 24,30,31 Within the variety of wearable devices, the Xiaomi Mi Band devices are one of the most attractive to the public due to their quality-price ratio. 32 Some research has used this device to measure the health status of different populations. ...
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Background: Lower quantity and poorer sleep quality are common in most older adults, especially for those who live in a nursing home. The use of wearable devices, which measure some parameters such as the sleep stages, could help to determine the influence of sleep quality in daily activity among nursing home residents. Therefore, this study aims to analyse the influence of sleep and its changes concerning the health status and daily activity of older people who lived in a nursing home, by monitoring the participants for a year with Xiaomi Mi Band 2. Methods: This is a longitudinal study set in a nursing home in [Details omitted for double-anonymized peer reviewed]. The Xiaomi Mi Band 2 will be used to measure biomedical parameters and different assessment tools will be administered to participants for evaluating their quality of life, sleep quality, cognitive state, and daily functioning. Results: A total of 21 nursing home residents participated in the study, with a mean age of 86.38 ± 9.26. The main outcomes were that sleep may influence daily activity, cognitive state, quality of life, and level of dependence in activities of daily life. Moreover, environmental factors and the passage of time could also impact sleep. Conclusions: Xiaomi Mi Band 2 could be an objective tool to assess the sleep of older adults and know its impact on some factors related to health status and quality of life of older nursing homes residents. Trial Registration: NCT04592796 (Registered 16 October 2020) Available on: https://clinicaltrials.gov/ct2/show/NCT04592796.
... As for sleep, because this study did not consider the sleep stages, we will only evaluate the accuracy of sleep total time assessment for the used devices. As validated by de Zambotti et al., 82 FC2 overestimated TST by 9 min when compared with polysomnography (p < .05). In the same way, 83 compared FC3 and found an inverse conclusion, with an underestimation of TST of about 11 minutes. ...
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Background: Heart rate (HR), especially at nighttime, is an important biomarker for cardiovascular health. It is known to be influenced by overall physical fitness, as well as daily life physical or psychological stressors like exercise, insufficient sleep, excess alcohol, certain foods, socialization, or air travel causing physiological arousal of the body. However, the exact mechanisms by which these stressors affect nighttime HR are unclear and may be highly idiographic (i.e. individual-specific). A single-case or "n-of-1" observational study (N1OS) is useful in exploring such suggested effects by examining each subject's exposure to both stressors and baseline conditions, thereby characterizing suggested effects specific to that individual. Objective: Our objective was to test and generate individual-specific N1OS hypotheses of the suggested effects of daily life stressors on nighttime HR. As an N1OS, this study provides conclusions for each participant, thus not requiring a representative population. Methods: We studied three healthy, nonathlete individuals, collecting the data for up to four years. Additionally, we evaluated model-twin randomization (MoTR), a novel Monte Carlo method facilitating the discovery of personalized interventions on stressors in daily life. Results: We found that physical activity can increase the nighttime heart rate amplitude, whereas there were no strong conclusions about its suggested effect on total sleep time. Self-reported states such as exercise, yoga, and stress were associated with increased (for the first two) and decreased (last one) average nighttime heart rate. Conclusions: This study implemented the MoTR method evaluating the suggested effects of daily stressors on nighttime heart rate, sleep time, and physical activity in an individualized way: via the N-of-1 approach. A Python implementation of MoTR is freely available.
... This sensor was selected due to its long battery life that enabled full week recording without charging, as well as its established reliability and validity in measuring diverse features of daily-life including physical activity, sleep and stress. 29,50,51 Participants wore the sensor on their non-dominant hand, two cm above the styloid process of the radius, such that it was securely in contact with the skin, but not tight enough to restrict blood flow or cause any inconvenience. Throughout study period, all sensor related notifications and reminders were disabled and participants received no feedback regarding their behavior and habits including sleep and physical activity. ...
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Background Chronic stress is a highly prevalent condition that may stem from different sources and can substantially impact physiology and behavior, potentially leading to impaired mental and physical health. Multiple physiological and behavioral lifestyle features can now be recorded unobtrusively in daily-life using wearable sensors. The aim of the current study was to identify a distinct set of physiological and behavioral lifestyle features that are associated with elevated levels of chronic stress across different stress sources. Methods For that, 140 healthy female participants completed the Trier inventory for chronic stress (TICS) before wearing the Fitbit Charge3 sensor for seven consecutive days while maintaining their daily routine. Physiological and lifestyle features that were extracted from sensor data, alongside demographic features, were used to predict high versus low chronic stress with support vector machine classifiers, applying out-of-sample model testing. Results The model achieved 79% classification accuracy for chronic stress from a social tension source. A mixture of physiological (resting heart-rate, heart-rate circadian characteristics), lifestyle (steps count, sleep onset and sleep regularity) and non-sensor demographic features (smoking status) contributed to this classification. Conclusion As wearable technologies continue to rapidly evolve, integration of daily-life indicators could improve our understanding of chronic stress and its impact of physiology and behavior.
... Sleep features captured sleeping duration and patterns, which could indicate sleep disturbance (eg, insomnia or hypersomnia) associated with depression [55]. Please see Multimedia Appendix 1 (section A.1 [29,44,46,[56][57][58][59]) for details of features extracted from each sensor. ...
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Background: The coronavirus disease 2019 (COVID-19) pandemic has broad negative impact on physical and mental health of people with chronic neurological disorders such as multiple sclerosis (MS). Objective: We present a machine learning approach leveraging passive sensor data from smartphones and fitness trackers of people with MS to predict their health outcomes in a natural experiment during a state-mandated "stay-at-home" period due to a global pandemic. Methods: First, we extract features that capture behavioral changes due to the "stay-at-home" order. Then, we adapt and apply an existing algorithm to these behavioral change features to predict the presence of depression, high global MS symptom burden, severe fatigue, and poor sleep quality during the "stay-at-home" period. Results: Using data collected between November 2019 and May 2020, algorithm detects depression with an accuracy of 82.5% (65% improvement over baseline; f1-score: 0.84), high global MS symptom burden with an accuracy of 90% (39% improvement over baseline; f1-score: 0.93), severe fatigue with an accuracy of 75.5% (22% improvement over baseline; f1-score: 0.80), and poor sleep quality with an accuracy of 84% (28% improvement over baseline; f1-score: 0.84). Conclusions: Our approach could help clinicians better triage patients with MS and potentially other chronic neurological disorders for interventions and aid patient self-monitoring in their own environment, particularly during extraordinarily stressful circumstances such as pandemics that would cause drastic behavioral changes. Clinicaltrial: Not Applicable.
... For example, Fitbit has been shown in the literature to perform well in sleep monitoring in healthy adults. In particular, Fitbit Charge 2 showed promise [2] in distinguishing sleep/wake state (0.96 sensitivity, i.e., accuracy to detect sleep, and 0.61 specificity, i.e., accuracy to detect wake) and sleep stage composition relative to PSG, especially for REM sleep. In OSA subjects, instead, Fitbit devices still have insufficient accuracy. ...
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Obstructive sleep apnea (OSA) is a common sleep disorder and polysomnography (PSG) is the gold standard for its diagnosis and treatment monitoring. There are nowadays several activity trackers measuring sleep quality through the detection of sleep stages. To allow an easier monitoring of the treatment efficacy at home, this work explores the possibility of using one of those commercial smart-bands. To this aim, we studied the signals provided by PSG and a Fitbit smart-band on 26 consecutive patients, admitted to the hospital after the diagnosis of OSA, and submitted to ventilation or positional treatment. They underwent monitoring for three nights (basal, titration, and control). We developed both a visualization software allowing doctors to visually compare the two hypnograms, and a set of statistics for assessing the concordance of the two methods. Results indicate that Fitbit can detect normal sleep patterns, while it is less able to detect the abnormal ones.
... The FitBit Charge 4 will collect data on sleep duration, 27 heart rate and step count. This is worn continuously on the wrist, including at night to determine when participants were awake or asleep. ...
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Introduction: Hypoglycaemia is a significant burden to people living with diabetes and an impediment to achieving optimal glycaemic outcomes. The use of continuous glucose monitoring CGM has improved capacity to assess duration and level of hypoglycaemia. The personal impact of sensor-detected hypoglycaemia SDH is unclear. Hypo-METRICS is an observational study designed to define the threshold and duration of sensor glucose that provides the optimal sensitivity and specificity for events that people living with diabetes experience as hypoglycaemia. Methods: We will recruit 600 participants: 350 with insulin-treated type 2 diabetes, 200 with type 1 diabetes and awareness of hypoglycaemia and 50 with type 1 diabetes and impaired awareness of hypoglycaemia who have recent experience of hypoglycaemia. Participants will wear a blinded CGM device and an actigraphy monitor to differentiate awake and sleep times for 10 weeks. Participants will be asked to complete three short surveys each day using a bespoke mobile phone app, a technique known as ecological momentary assessment. Participants will also record all episodes of self-detected hypoglycaemia on the mobile app. We will use particle Markov chain Monte Carlo optimization to identify the optimal threshold and duration of SDH that have optimum sensitivity and specificity for detecting patient reported hypoglycaemia. Key secondary objectives include measuring the impact of symptomatic and asymptomatic SDH on daily functioning and health economic outcomes. Ethics and dissemination: The protocol was approved by local ethical boards in all participating centres. Study results will be shared with participants, in peer-reviewed journal publications and conference presentations.
... 9 The Fitbit Charge 2 TM uses a proprietary algorithm to combine speed measurements and heart rate variability in sleep assessment, which has been reported to detect sleep-wake states in healthy adults accurately, but with limited detection of N3 sleep. 10 FBA is the successor to the Fitbit charge 2 TM and is a downsized and minimized version of it. While the FBA has been reported to detect the sleep-wake state in healthy adults accurately, 11 it has been reported to be insufficiently accurate in specific clinical populations 12 . ...
Article
Purpose: Sleep is an essential factor for athletes, and it is important to intervene in sleep to manage it. We need a device that can evaluate sleep easily and constantly. Consumer wearable devices can be useful tools for athletes. In order to use consumer wearable devices in clinical research, it is essential to conduct a validation study. Thus, we conducted a validation study to assess the Fitbit Alta HRTM (FBA)- a consumer wearable device with an accelerometer and a heart rate monitor to detect sleep stages and quality against electroencephalographic (EEG) studies in athletes. Patients and methods: Forty college athletes participated in the study. EEG was applied to participants simultaneously while wearing FBA. Results: Regarding sleep parameters, there was a strong correlation between the total sleep time (TST)-EEG and the TST-Fitbit (r = 0.83; p < 0.001). Regarding the sleep stages, there was a modest correlation between the N3 sleep-EEG and the N3 sleep-Fitbit (r = 0.68; p < 0.001). In addition, there was a strong correlation between the percentage of N3 sleep in between sleep onset and initial rapid eye movement sleep-EEG and those on Fitbit (r = 0.73; p < 0.001). Conclusion: These results demonstrate that FBA facilitates sleep monitoring and exhibits acceptable agreement with EEG. Therefore, FBA is a useful tool in athletes' sleep management.
... 24 In environmental researches, very few studies discussed the choice of objective sleep assessment before experiment, though it did have a certain impact on the results. Zambotti et al. 25 evaluated the performance of a wristband (Fitbit Charge 2™), against polysomnography (PSG) in healthy adults, mainly from the perspective of sleep staging, and they pointed out that Fitbit showed good prospects in detecting REM sleep but limitations in N3 detection. ...
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To date, although many studies had focused on the impact of environmental factors on sleep, how to choose the proper assessment method for objective sleep quality was often ignored, especially for healthy subjects in bedroom environment. In order to provide methodological guidance for future research, this paper reviewed the assessments of objective sleep quality applied in environmental researches, compared them from the perspective of accuracy and interference, and statistically analyzed the impact of experimental type and subjects’ information on method selection. The review results showed that, in contrast to polysomnography (PSG), the accuracy of actigraphy (ACT), respiratory monitoring-oxygen saturation monitoring (RM-OSM), and electrocardiograph (ECG) could reach up to 97, 80.38, and 79.95%, respectively. In terms of sleep staging, PSG and ECG performed the best, ACT the second, and RM-OSM the worst; as compared to single methods, mix methods were more accurate and better at sleep staging. PSG interfered with sleep a great deal, while ECG and ACT could be non-contact, and thus the least interference with sleep was present. The type of experiment significantly influenced the choice of assessment method (p < 0.001), 85.3% of researchers chose PSG in laboratory study while 82.5% ACT in field study; moreover, PSG was often used in a relatively small number of young subjects, while ACT had a wide applicable population. In general, researchers need to pay more attention at selection of assessments in future studies and this review can be used as a reliable reference for experimental design.
... The raw data of daily step count obtained using the 3-axis accelerometer of Fitbit ® , which allows the device to determine the frequency, duration, intensity, and patterns of a participant's movement, were uploaded to the Fitbit ® database [22,23]. The Fitbit ® device collects steps and heart rate in minus epochs, and measures sleep/wake time and sleep stages across four levels-"wake," "light," "deep," and "REM" (Figure 1) [24]. The heart rate value corresponding to 0 in Figure 1 means that a user did not wear a device at that time. ...
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Abstract: We compared the improvement in components of metabolic syndrome (MS) before and after lifestyle modification, as determined by daily step counts (on a wrist-worn Fitbit®) in participants with and without MS recruited from volunteers attending medical health checkup programs. A linear mixed model was used to analyze the change in MS components between participants with and without MS by group � time interaction. Multiple logistic regression analysis after adjustment for confounders was used to obtain odds ratios (ORs) and 95% confidence intervals (CIs) for improvements in MS components per 1000-steps/day increments. Waist circumference, triglycerides, fasting plasma glucose, and diastolic blood pressure were significantly different between participants with and without MS (group � time: p = 0.010, p < 0.001, p = 0.025, and p = 0.010, respectively). Multivariable-adjusted ORs (95% CI) of improvement in MS components per 1000-steps/day increments were 1.24 (1.01–1.53) in participants with and 1.14 (0.93–1.40) in participants without MS. Walking improved MS components more in individuals with than without MS. From a public health perspective, walking should be encouraged for high-risk MS individuals.
... Major professional societies such as the American Academy of Sleep Medicine and Sleep Research Society have taken much interest in shaping the conversation around the Nature and Science of Sleep 2022:14 493-516 493 present and future of sleep-tracking technologies -eg, how to evaluate device performance, 2 and determining guidelines for whether to potentially use data from commercial sleep-tracking technologies in clinical sleep medicine practice. 3,4 Encouragingly, many recent primary studies [5][6][7][8][9][10][11][12][13][14] and reviews 2,15-18 evaluating the performance of the newest commercial devices have demonstrated their improved sleep-tracking performance against many of the earliest device models released on the consumer market~5-10 years ago. [19][20][21][22] Several studies have even found that, compared with the goldstandard PSG, some commercial devices perform as well as or better than the current standard mobile sleep measurement methodology of research-grade actigraphy. ...
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Purpose: Commercial wearable sleep-tracking devices are growing in popularity and in recent studies have performed well against gold standard sleep measurement techniques. However, most studies were conducted in controlled laboratory conditions. We therefore aimed to test the performance of devices under naturalistic unrestricted home sleep conditions. Participants and methods: Healthy young adults (n = 21; 12 women, 9 men; 29.0 ± 5.0 years, mean ± SD) slept at home under unrestricted conditions for 1 week using a set of commercial wearable sleep-tracking devices and completed daily sleep diaries. Devices included the Fatigue Science Readiband, Fitbit Inspire HR, Oura ring, and Polar Vantage V Titan. Participants also wore a research-grade actigraphy watch (Philips Respironics Actiwatch 2) for comparison. To assess performance, all devices were compared with a high performing mobile sleep electroencephalography headband device (Dreem 2). Analyses included epoch-by-epoch and sleep summary agreement comparisons. Results: Devices accurately tracked sleep-wake summary metrics (ie, time in bed, total sleep time, sleep efficiency, sleep latency, wake after sleep onset) on most nights but performed best on nights with higher sleep efficiency. Epoch-by-epoch sensitivity (for sleep) and specificity (for wake), respectively, were as follows: Actiwatch (0.95, 0.35), Fatigue Science (0.94, 0.40), Fitbit (0.93, 0.45), Oura (0.94, 0.41), and Polar (0.96, 0.35). Sleep stage-tracking performance was mixed, with high variability. Conclusion: As in previous studies, all devices were better at detecting sleep than wake, and most devices compared favorably to actigraphy in wake detection. Devices performed best on nights with more consolidated sleep patterns. Unrestricted sleep TIB differences were accurately tracked on most nights. High variability in sleep stage-tracking performance suggests that these devices, in their current form, are still best utilized for tracking sleep-wake outcomes and not sleep stages. Most commercial wearables exhibited promising performance for tracking sleep-wake in real-world conditions, further supporting their consideration as an alternative to actigraphy.
... Oura (median kappa = 0.54) and Fitbit (median kappa = 0.51) have much lower kappas than PhyMask (median kappa = 0.77). Low accuracy of these sleep trackers in detecting sleep stages has also been observed by previous studies [35,36]. Since we do not have access to the raw data captured by Oura and Fitbit sleep tracking algorithms, we cannot fully analyze their behaviour. ...
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Clinical-grade wearable sleep monitoring is a challenging problem since it requires concurrently monitoring brain activity, eye movement, muscle activity, cardio-respiratory features, and gross body movements. This requires multiple sensors to be worn at different locations as well as uncomfortable adhesives and discrete electronic components to be placed on the head. As a result, existing wearables either compromise comfort or compromise accuracy in tracking sleep variables. We propose PhyMask, an all-textile sleep monitoring solution that is practical and comfortable for continuous use and that acquires all signals of interest to sleep solely using comfortable textile sensors placed on the head. We show that PhyMask can be used to accurately measure all the signals required for precise sleep stage tracking and to extract advanced sleep markers such as spindles and K-complexes robustly in the real-world setting. We validate PhyMask against polysomnography and show that it significantly outperforms two commercially-available sleep tracking wearables – Fitbit and Oura Ring.
... In another validation study by de Zambotti et al. [28], the authors assessed the performance of a consumer multi-sensory wristband (Fitbit Charge 2) for sleep-stage classification versus PSG. The Fitbit device can monitor time spent awake, light sleep, deep sleep, and REM sleep, in addition to sleep/wake states. ...
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Polysomnography is the gold-standard method for measuring sleep but is inconvenient and limited to a laboratory or a hospital setting. As a result, the vast majority of patients do not receive a proper diagnosis. In an attempt to solve this issue, sleep experts are continually looking for unobtrusive and affordable alternatives that can provide longitudinal sleep tracking. Collecting longitudinal data on sleep can accelerate epidemiological studies exploring the effect of sleep on health and disease. These alternatives can be in the form of wearables (e.g., actigraphs) or nonwearable (e.g., under-mattress sleep trackers). To this end, this paper aims to review the several attempts made by researchers toward unobtrusive sleep monitoring, specifically sleep cycle. We have performed a literature search between 2016 and 2021 and the following databases were used for retrieving related articles to unobtrusive sleep cycle monitoring: IEEE, Google Scholar, Journal of Clinical Sleep Medicine (JCSM), and PubMed Central (PMC). Following our survey, although existing devices showed promising results, most of the studies are restricted to a small sample of healthy individuals. Therefore, a broader scope of participants should be taken into consideration during future proposals and assessments of sleep cycle tracking systems. This is because factors such as gender, age, profession, and social class can largely affect sleep quality. Furthermore, a combination of sensors, e.g., smartwatches and under-mattress sleep trackers, are necessary to achieve reliable results. That is, wearables and nonwearable devices are complementary to each other, and so both are needed to boost the field of at-home sleep monitoring.
... It was also one of the two commercial sleep trackers that had been validated by independent testing [14,15]. In particular, Fitbit Charge 2 TM in a validation study had shown 0.96 sensitivity in detecting sleep and a 0.61 specificity in detecting wake [16]. Fitbit models have their inherent limitations and tend to overestimate total sleep time and sleep efficiency, and underestimate wakefulness after sleep onset (WASO) [13]. ...
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Introduction: Sleep tracker data have not been utilized routinely in sleep-related disorders and their management. Sleep-related disorders are common in primary care practice and incorporating sleep tracker data may help in improving patient care. We conducted a pilot study to assess the feasibility of a sleep program using the Fitbit Charge 2™ device and SleepLife® application. The main aim of the study was to examine whether a program using a commercially available wearable sleep tracker device providing objective sleep data would improve communication in primary care settings between patients and their providers. Secondary aims included whether patient satisfaction with care would improve as result of the program. Methods: A prospective, randomized, parallel group, observational pilot study was conducted in 20 primary care clinics in Indianapolis, IN from June 2018 to February 2019. Inclusion criteria included patients over the age of 18, have a diagnosis of insomnia identified by electronic medical record and/or a validated questionnaire, and were on a prescription sleep aid. The study was not specific to any sleep aid prescription, branded or generic, and was not designed to evaluate a drug or drug class. Each primary care clinic was randomized to either the SleepLife® intervention or the control arm. All patients were provided with a Fitbit Charge 2™ device. Only patients in the intervention arm were educated on how to use the SleepLife® application. Physicians in the intervention arm were set up with the SleepLife® portal on their computers. Results: Forty-nine physicians and 75 patients were enrolled in the study. Patients had a mean age of 57 (SD 12.8) years and 61% were female. Mean age of physicians was 47 (SD 10.6) years. Patients showed high rates of involvement in the program with 83% completing all survey questions. Physician survey completion rate was 55%. Only one physician logged into the SleepLife portal to check their patients' sleep status. At the end of the 6-week intervention, patients' composite general satisfaction scores with sleep health management decreased significantly in the intervention arm when compared to controls (p = 0.03). Patients' satisfaction with communication also decreased significantly in the intervention group (p = 0.01). The sleep outcomes, which were calculated on the basis of study questionnaire answers, improved significantly in the intervention group as compared to the control group (p = 0.04). Physician communication satisfaction scores remained unchanged (p = 0.12). Conclusions: SleepLife® and its related physician portal can facilitate physician-patient communication, and it captures patient sleep outcomes including behaviors and habits. Patients were highly engaged with the program, while physicians did not demonstrate engagement. The study design and questionnaires do not specifically address the reasons behind the decreased patient satisfaction with care and communication, but it was perceived to be a result of physician non-responsiveness. Sleep quality scores on the other hand showed an improvement among SleepLife® users, suggesting that patients may have implemented good sleep practices on their own. Given that it was a feasibility study, and the sample size was small, we were not able to make major inferences regarding the difference between sleep disorder types. Additionally, we excluded patients with a history of alcohol use, substance abuse, or depression because of concerns that they may affect sleep independently. To promote the growth of technology in primary care, further research incorporating results from this study and physician engagement techniques should be included.
... Options such as wearable acceleromaters, or fitness trackers, or Page 4024 even under-mattress sensors which could potentially offer objective data on sleep outcomes and should be employed in future studies. [18][19][20][21] ...
Article
Background: Night float call systems are becoming increasingly common at training programs with the goal of reducing fatigue related to sleep deprivation and sleep disturbance. Previous studies have shown that trainees obtain less sleep during the night float rotation and have decreased sleep efficiency for several days after the rotation. The impact on physical and emotional well-being has not been documented. Methods: Twenty-seven anesthesia residents were enrolled in a study using wearable sleep and activity trackers and National Institutes of Health Patient-Reported Outcome Measurement Information System (NIH PROMIS) surveys for sleep disturbance, fatigue, and positive affect to record data the week before ("baseline"), during ("night float"), and 1 week after ("recovery") their night float rotation. Each subject's data during the night float week and recovery week were compared to his or her own baseline week data using a paired, nonparametric analysis. The primary outcome variable was the change in average daily sleep hours during the night float week compared to the baseline week. Average daily rapid eye movement (REM) sleep, daily steps, and NIH PROMIS scores comparing night float and recovery weeks to baseline week were prespecified secondary outcomes. NIH PROMIS scores range from 0 to 100 with 50 as the national mean and more of the construct having a higher score. Results: There was no difference in average daily sleep hours between the night float and the baseline weeks (6.7 [5.9-7.8] vs 6.7 [5.5-7.7] hours, median [interquartile range]; P = .20). Residents had less REM sleep during the night float compared to the baseline weeks (1.1 [0.7-1.5] vs 1.4 [1.1-1.9] hours, P = .002). NIH PROMIS fatigue scores were higher during the night float than the baseline week (58.8 [54.6-65.1] vs 48.6 [46.0-55.1], P = .0004) and did not return to baseline during the recovery week (51.0 [48.6-58.8], P = .029 compared to baseline). Sleep disturbance was not different among the weeks. Positive affect was reduced after night float compared to baseline (39.6 [35.0-43.5] vs 44.8 [40.1-49.6], P = .0009), but returned to baseline during the recovery week (43.6 [39.6-48.2], P = .38). Conclusions: The residents slept the same number of total hours during their night float week but had less REM sleep, were more fatigued, and had less positive affect. All of these resolved to baseline except fatigue, that was still greater than the baseline week. This methodology appears to robustly capture psychophysiological data that might be useful for quality initiatives.
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Objectives Sleep disturbances are associated with both the onset and progression of depressive disorders. It is important to capture day-to-day variability in sleep patterns; irregular sleep is associated with depressive symptoms. We used sleep efficiency, measured with wearable devices, as an objective indicator of daily sleep variability. Materials and methods The total sample consists of 100 undergraduate and graduate students, 60% of whom were female. All were divided into three groups (with major depressive disorder, mild depressive symptoms, and controls). Self-report questionnaires were completed at the beginning of the experiment, and sleep efficiency data were collected daily for 2 weeks using wearable devices. We explored whether the mean value of sleep efficiency, and its variability, predicted the severity of depression using dynamic structural equation modeling. Results More marked daily variability in sleep efficiency significantly predicted levels of depression and anxiety, as did the average person-level covariates (longer time in bed, poorer quality of life, lower extraversion, and higher neuroticism). Conclusion Large swings in day-to-day sleep efficiency and certain clinical characteristics might be associated with depression severity in young adults.
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There is a global aging population requiring the need for the right tools that can enable older adults' greater independence and the ability to age at home, as well as assist healthcare workers. It is feasible to achieve this objective by building predictive models that assist healthcare workers in monitoring and analyzing older adults' behavioral, functional, and psychological data. To develop such models, a large amount of multimodal sensor data is typically required. In this paper, we propose MAISON, a scalable cloud-based platform of commercially available smart devices capable of collecting desired multimodal sensor data from older adults and patients living in their own homes. The MAISON platform is novel due to its ability to collect a greater variety of data modalities than the existing platforms, as well as its new features that result in seamless data collection and ease of use for older adults who may not be digitally literate. We demonstrated the feasibility of the MAISON platform with two older adults discharged home from a large rehabilitation center. The results indicate that the MAISON platform was able to collect and store sensor data in a cloud without functional glitches or performance degradation. This paper will also discuss the challenges faced during the development of the platform and data collection in the homes of older adults. MAISON is a novel platform designed to collect multimodal data and facilitate the development of predictive models for detecting key health indicators, including social isolation, depression, and functional decline, and is feasible to use with older adults in the community.
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Monitoring sleep in real-world conditions requires bespoke equipment, e.g., Actigraphs or similar. However, such equipment is relatively expensive and not always available for large-scale field research. This study tests the possibility that sleep in real-world conditions can be monitored, sufficiently accurately, by tandems of commonly used smartphones (SP) and smartwatches (SW). 10 adult participants were asked to wear Actigraph, and SW for 30 consecutive nights. The accumulated records were analyzed using bi-variate statistics, mixed modeling and epoch-by-epoch analysis. A high degree of correspondence was found between Actigraph, SP/SW, and self-report records (R2 = 0.968–0.983). Although the mixed modeling also indicated high collinearity between Actigraphs and SP/SW tandems (b = 0.991; p < 0.001), it was also shown that SP/SW tandems add ⁓21.9 min to the Actigraph measurements while the self-reports were found to be even less accurate, adding ⁓39.9 min. Concurrently, the epoch-by-epoch analysis showed a good agreement between different types of measurements, varying between 81% and 100%. As we conclude, widely available and affordable SPs and SWs can help researchers to generate fairly reliable data for large-scale field studies, albeit measurement corrections need to be applied. Yet, estimates, obtained from sleep diaries, need to be treated with caution.
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Sleep insufficiency is a risk factor for mental and physical ill-health. In recent years, research has attributed sleep insufficiency to problematic smartphone use (PSU). In addition, research has indicated a relationship between sleep and the construct of mental toughness (MT). However, previous research exploring the relationship between sleep, PSU and MT has relied on self-report measures. Therefore, this study aimed to explore the tentative links between sleep, PSU and MT by gathering objective data. 2053 participants completed measures of sleep quality, PSU and MT. Objective smartphone usage data were collected using pre-installed smartphone applications. A sub-sample of 614 participants provided sleep duration data from validated sleep tracking devices. In line with previous research, sleep quality was found to correlate weakly with both MT and PSU. While several significant correlations emerged when objective data were explored, in all cases, the effect sizes were negligible. This study does not support the claim that PSU has a clinically meaningful impact upon sleep duration. Sleep hygiene recommendations with more well-established empirical support should be prioritised during sleep promotion efforts.
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Background Many studies show positive bidirectional associations between physical activity (PA) and sleep at the between-person level. There is an increased interest in investigating these associations at the within-person level. Few studies examined the effects of time-varying moderators on the within-person bidirectional associations between PA and sleep. This study aimed to examine the bidirectional within-person day-level associations between activity levels and self-reported sleep duration and explore the moderating effects of perceived stress on these day-level associations.Method Data from 158 women that included 7-day free-living monitoring over 4 measurement periods was analyzed using multilevel modeling to explore the moderating effects of daily stress on the bidirectional, within-person associations between activity levels and self-reported sleep duration. Moderate-to-vigorous PA (MVPA) and sedentary behavior (SB) were estimated from a waist-worn accelerometer. Self-reported sleep duration and perceived stress were collected by ecological momentary assessment.ResultsNo significant within-person associations between MVPA minutes and self-reported sleep duration were found in either direction. However, engaging in more MVPA than one’s usual level was associated with longer sleep later that night when perceived stress was higher than usual (p = .04). Bidirectional negative within-person association between SB minutes and self-reported sleep duration was found (ps < .01). The negative association between SB and sleep duration later that night was stronger when perceived stress was lower than usual (p = .01).Conclusion Daily stress played an important role in the day-to-day associations between activity levels and sleep.
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A widely accepted view in memory research is that previously acquired information can be reactivated during sleep, leading to persistent memory storage. Targeted memory reactivation (TMR) was developed as a technique whereby specific memories can be reactivated during sleep using a sensory stimulus linked to prior learning. As a research tool, TMR can improve memory, raising the possibility that it may be useful for cognitive enhancement and clinical therapy. A major challenge for the expanded use of TMR is that a skilled operator must manually control stimulation, which is impractical in many settings. To address this limitation, we developed the SleepStim system for automated TMR in the home. SleepStim includes a smartwatch to collect movement and heart-rate data, plus a smartphone to emit auditory cues. A machine-learning model identifies periods of deep sleep and triggers TMR sounds within these periods. We tested whether this system could replicate the spatial-memory benefit of in-laboratory TMR. Participants learned locations of objects on a grid, and then half of the object locations were reactivated during sleep over 3 nights. Recall was tested each morning. In an experiment with 61 participants, the TMR effect was not significant but varied systematically with stimulus intensity; low-intensity but not high-intensity stimuli produced memory benefits. In a second experiment with 24 participants, we limited stimulus intensity and found that TMR reliably improved spatial memory, consistent with effects observed in laboratory studies. We conclude that SleepStim can effectively accomplish automated TMR, and that avoiding sleep disruption is critical for TMR benefits.
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Wearable technology, which can continuously and remotely monitor physiological and behavioral parameters by incorporated into clothing or worn as an accessory, introduces a new era for ubiquitous health care. With big data technology, wearable data can be analyzed to help long-term cardiovascular care. This review summarizes the recent developments of wearable technology related to cardiovascular care, highlighting the most common wearable devices and their accuracy. We also examined the application of these devices in cardiovascular healthcare, such as the early detection of arrhythmias, measuring blood pressure, and detecting prevalent diabetes. We provide an overview of the challenges that hinder the widespread application of wearable devices, such as inadequate device accuracy, data redundancy, concerns associated with data security, and lack of meaningful criteria, and offer potential solutions. Finally, the future research direction for cardiovascular care using wearable devices is discussed.
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This descriptive study aimed to describe sleep patterns and sleep problems among older persons with dementia and management by their caregivers. Purposive sampling was used to recruit 88 community-dwelling older persons with any stage of dementia and 88 caregivers. The research instruments for older participants included the Demographic Questionnaire, sleep diaries, and wrist sleep trackers; those for caregivers included the Demographic Questionnaire, and the Caregivers’ Management of Sleep Problems in Older Persons with Dementia. The data were collected from June to August 2019 and then analyzed using descriptive statistics and chi-square. Almost all of the sleep data showed the polyphasic (sleeping many times per day) sleep pattern, while a few had biphasic (sleeping twice per day) and monophasic (sleeping once per day) sleep patterns. Sleep problems in the older sample included sleep-related breathing disorder (snoring), sleep talking, hyper-somnolence, and wake after sleep onset, respectively. Mainly, non-pharmacological interventions were used for sleep problems of older persons with dementia by caregivers, including promotion of relaxation, light exposure, sleep hygiene, and physical activities. However, one quarter used medications prescribed by doctors. Healthcare providers could use the results from this study to plan interventions for reducing the polyphasic sleep pattern, decreasing hypersomnia and excessive daytime sleepiness, and developing an educational program for caregivers about the management of sleep problems in older persons with dementia appropriately.
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Background Many studies have reported a possible strong relationship between poor sleep quality, sleep disruption, sleep disorders, and erectile dysfunction (ED). Aim This study aimed to investigate the relationship between sleep quality and ED. Methods Patients diagnosed with ED by the International Index of Erectile Function-5 (IIEF-5) questionnaire and 72 healthy adult men were included. Participants completed the questionnaire, underwent a detailed physical examination, and provided blood samples. All enrolled subjects then wore the Fitbit Charge 2 that monitored sleep throughout the night. Outcomes Primary outcome measures included scores on the IIEF-5, General Anxiety Disorder-7 (GAD-7) scale, Patient Health Questionnaire-9 (PHQ-9), Pittsburgh Sleep Quality Index (PSQI), and sleep monitoring parameters obtained from Fitbit Charge 2. Results Finally, a total of 107 ED patients and 72 healthy adult men were enrolled in this study. Univariate analysis indicated that the GAD-7 (P < .001), PHQ-9 (P < .001), and PSQI scores (P < .001) significantly differed according to the presence/absence of ED. Further multiple logistic regression analysis showed that the PHQ-9 (odds ratio [OR]: 1.227, 95% confidence interval [CI]: 1.070-1.407; P = .003) and PSQI scores (OR: 1.220, 95%CI: 1.116-1.334; P < .001) were independent risk factors for ED. Analysis of objective sleep monitoring parameters showed that total sleep time (TST) (P = .001), sleep onset latency (SOL) (P = .026), deep sleep (N3) duration (P = .011) and rapid eye movement (REM) sleep duration (P < .001) were significantly differed between the 2 groups, with durations in the ED group significantly lower than those in the non-ED group. In addition, receiver operating characteristic (ROC) curve analysis indicated that the REM sleep duration had the highest area under the curve (AUC: 0.728) of all sleep parameters, with a P value < .001, a sensitivity of 72.2% and a specificity of 73.8%. Clinical Implications Urologists and andrologists should be aware of impacted sleep quality and depression in ED patients. Strengths & Limitations The strength of this study is that the relationship between sleep quality and ED was assessed with both a subjective scale and an objective sleep monitoring tool. However, our study only described an association between sleep quality and ED and did not establish a causal relationship. Conclusion Sleep parameters are strongly associated with ED, indicating that poor sleep quality may increase the likelihood of ED. Wu X, Zhang Y, Zhang W, et al. The Association Between Erectile Dysfunction and Sleep Parameters: Data from a Prospective, Controlled Cohort. J Sex Med 2022;XX:XXX–XXX.
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Children in the United States sleep less than the recommended amount and sleep deficiencies may be worse among disadvantaged children. Prior studies that compared sleep time in children of different race/ethnic groups mostly relied on questionnaires or were limited to small sample sizes. Our study takes advantage of the Adolescent Brain Cognitive Development study to compare total sleep time using a week of actigraphy data among American children (n = 4,207, 9 to 13 y old) of different racial/ethnic and income groups. We also assessed the effects of neighborhood deprivation, experience of discrimination, parent’s age at child’s birth, body mass index (BMI), and time the child fell asleep on sleep times. Daily total sleep time for the sample was 7.45 h and race/ethnicity, income, sex, age, BMI, were all significant predictors of total sleep time. Black children slept less than White children (∼34 min; Cohen’s d = 0.95), children from lower income families slept less than those from higher incomes (∼16 min; Cohen’s d = 0.44), boys slept less than girls (∼7 min; Cohen’s d = 0.18), and older children slept less than younger ones (∼32 min; Cohen’s d = 0.91); mostly due to later sleep times. Children with higher BMI also had shorter sleep times. Neither area deprivation index, experience of discrimination, or parent’s age at child’s birth significantly contributed to sleep time. Our findings indicate that children in the United States sleep significantly less than the recommended amount for healthy development and identifies significant racial and income disparities. Interventions to improve sleep hygiene in children will help improve health and ameliorate racial disparities in health outcomes.
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Background Intensive longitudinal methods (ILMs) for collecting self-report (e.g., daily diaries, ecological momentary assessment) and passive data from smartphones and wearable sensors provide promising avenues for improved prediction of depression and suicidal ideation (SI). However, few studies have utilized ILMs to predict outcomes for at-risk, non-clinical populations in real-world settings. Methods Medical interns (N = 2881; 57 % female; 58 % White) were recruited from over 300 US residency programs. Interns completed a pre-internship assessment of depression, were given Fitbit wearable devices, and provided daily mood ratings (scale: 1–10) via mobile application during the study period. Three-step hierarchical logistic regressions were used to predict depression and SI at the end of the first quarter utilizing pre-internship predictors in step 1, Fitbit sleep/step features in step 2, and daily diary mood features in step 3. Results Passively collected Fitbit features related to sleep and steps had negligible predictive validity for depression, and no incremental predictive validity for SI. However, mean-level and variability in mood scores derived from daily diaries were significant independent predictors of depression and SI, and significantly improved model accuracy. Limitations Work schedules for interns may result in sleep and activity patterns that differ from typical associations with depression or SI. The SI measure did not capture intent or severity. Conclusions Mobile self-reporting of daily mood improved the prediction of depression and SI during a meaningful at-risk period under naturalistic conditions. Additional research is needed to guide the development of adaptive interventions among vulnerable populations.
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Study objectives: Lack of sleep has been shown to be harmful to athletic and academic performance as well as health and well-being. The primary purpose of this study was to analyze the sleep and physical activity differences between U.S. high school student-athletes and non-athletes during a semester of school and competition. Methods: Participants included 34 student-athletes (18 males and 16 females), age 15.8 ± 0.8 years and 38 non-athletes (10 males and 28 females), age 16.3 ± 0.7 years. Objective sleep and physical activity outcomes were collected using Fitbit wrist-worn activity trackers for 8-14 consecutive days and nights, measuring total sleep time (TST), sleep efficiency (SE), bedtimes, wake times, and steps counted. Results: Student-athletes and non-athletes did not differ in TST (440.4 ± 46.4 vs. 438.1 ± 41.7 min, p = 0.82) and SE (93.6 ± 2.3 vs. 92.9 ± 2.3%, p = 0.20). Fitbit data revealed that 79% of student-athletes and 87% of non-athletes failed to get greater than the minimally recommended 8 hours of total sleep time per night. Student-athletes had significantly more steps per day (10,163 ± 2,035 vs. 8,418 ± 2,489, p < 0.01). Student-athletes had earlier bedtimes and wake times. Earlier bedtimes were significantly correlated with increased TST (p < 0.01). Earlier wake times were significantly correlated to increased steps per day (p < 0.01). Conclusions: Participation in high school sports may not have a detrimental effect on a student's sleep habits. High school students are not meeting the recommended 8-10 hours of sleep per night. Going to bed and waking up early were linked to healthier outcomes. Consistent and earlier sleep/wake schedules may optimize students sleep and health.
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Intelligent Robots as well as Affective (Emotional) Technologies are among the most important advances of the so-called era of the fourth industrial revolution, which arguably creates an ever-increasing fusion of the physical, digital, and biological worlds. But how is gender intertwined with these advances, as viewed under multiple lenses? More specifically, how are these advances promising to change the world of healthcare and wellbeing, and what research has taken place at the intersection of these advances with gender and healthcare? These are the main questions that will be tackled in this chapter, following a short historical introduction. A forward-looking discussion, integrating predicted developments with the concepts of the digital twin and the quantified self, illustrates a vision of a possible future—A future in which these technologies will catalyze an increasingly holistic and antireductionist understanding, simulation, and capacity for beneficial orchestrated interventions, not only at a single level but rather at the multiple levels, ranging from the biological and neural to the psychological and social and beyond, that constitute the foundations of health and the prerequisites for wellbeing.
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Personal informatics systems can help people promote their health and well-being. Recent studies have shown that such systems can be used to infer relevant health indicators such as, e.g., stress, anxiety, and sleeping habits. While automatic detection of sleep has been studied extensively, there is a lack of studies exploring how population and personalized models influence the performance of sleep detection. In this article, we address this challenge by investigating the recognition of sleep/wake stages and high/low sleep quality with a focus on the impact of personalized models. To evaluate our approach, we collect a dataset of physiological signals and self-reports about sleep/wake times and sleep quality score. The dataset contains 6557 hours of sensor data collected using wristbands from 16 participants over one month. Our results show that personalized models perform significantly better than population models for sleep quality recognition, and are comparably good for sleep stage detection. The balanced accuracy for sleep/wake and high/low sleep quality are 92.2% and 61.51%, which are significantly higher than baseline classifiers.
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Bu araştırmanın amacı, kalp atım hızı (KAH) ölçümünde fotopletismografi (PPG) teknolojisini kullanan Huawei Honor Band 5 (Huawei) ve Xiaomi Mi Smart Band 5 (Xiaomi) giyilebilir akıllı saatlerinin (GAS) KAH ölçümünde altın standart olarak referans alınan Polar V800 (Polar) saati karşısında geçerli veriler verip veremeyeceğinin kontrol edilmesidir. Araştırmaya, Erzincan Binali Yıldırım Üniversitesi (EBYÜ), Spor Bilimleri Fakültesi öğrencisi, 11’i kadın, 19’u erkek toplam 30 katılımcı (Yaş: 21,9±3 yıl, Boy: 172±9,5 cm, Kilo: 70,6±12,4 kg) gönüllülük esasına göre katılmıştır. Katılımcılara Polar, Huawei ve Xiaomi saatleri aynı anda ve farklı kollara takılmıştır. Polar saat takılı olduğu sağ kolda sabit kalırken, Huawei sağ ve Xiaomi ise sol kola takılmıştır. Katılımcıların Dinlenik Kalp Atım Hızları (DKAH) kaydedildikten sonra katılımcılar şiddeti sürekli artan Yo-Yo dinlenmeli koşu testine tabi tutularak ulaşabildikleri maksimum KAH’larının %75 ve %100’üne ulaşan değerleri ölçülmüştür. Her mekik sonunda katılımcıların üzerinde yer alan üç farklı saatten KAH ölçümleri alınarak kaydedilmiştir. Verilerin değerlendirilmesinde One-Sample T Testi, Pearson Korelasyon Katsayısı, Sınıfiçi Korelasyon Katsayısı (ICC) ve Bland-Altman Analizi kullanılmıştır. Araştırma sonuçlarına göre, katılımcıların DKAH ölçümlerinde bu üç saat arasında istatistiksel olarak anlamlı bir farklılık olmadığı (Polar: 81,3 atım/dk, Huawei: 81,9 atım/dk, Xiaomi: 81,1 atım/dk) (p>0.05), fakat katılımcıların maksimum KAH’larının %75 (Polar: 142,9 atım/dk, Huawei: 121,1 atım/dk, Xiaomi: 121,2 atım/dk) (p<0.05) ve %100’üne (Polar: 190,5 atım/dk, Huawei: 162 atım/dk, Xiaomi: 157,5 atım/dk) (p<0.05) denk gelen ölçümlerinde ise istatistiksel olarak anlamlı farklılıklar olduğu gözlemlenmiştir. Bu sonuçlara göre, KAH takibinde Huawei ve Xiaomi saatlerinin günlük kullanımlarının uygun olabileceği, ancak egzersiz sırasında sporcu gelişimi ve sağlığı açısından kullanımlarının uygun olmayacağı anlaşılmaktadır. The purpose of this study was to investigate whether Huawei Honor Band 5 (Huawei) and Xiaomi Mi Smart Band 5 (Xiaomi) could provide valid scores when compared the Polar V800 (Polar), that has been accepted as gold standard for heart rate assessment. In total, 11 females and 19 males (Age: 21,9±3 years, height: 172±9,5 cm, weight: 70,6±12,4 kg) individuals from Erzincan Binali Yıldırım University (EBYU) voluntarily participated in this study Participants wore Polar, Huawei and Xiaomi watches at the same time and on different wrists. The Polar watch is fixed on the right wrist, while Huawei is on the right and Xiaomi is on the left. After recording resting heart rate, participants were asked to perform Yo-Yo intermittent recovery test protocol. During the test 75% and 100% of maximal heart rate scores were recorded. Each shuttle result was measured. One sample t-test, Pearson Correlation Coefficient, Intra Class Correlation Coefficient and Bland-Altman were used for statistical analysis. Results showed that there were no significant differences among each other at resting conditions (Polar: 81,3 bpm, Huawei: 81,9 bpm, Xiaomi: 81,1 bpm) (p>0.05). However significant findings were observed in both 75% (Polar: 142,9 bpm, Huawei: 121,1 bpm, Xiaomi: 121,2 bpm (p<0.05) and 100% (Polar: 190,5 bpm, Huawei: 162 bpm, Xiaomi: 157,5 bpm) (p<0.05) of their maximal heart rate. According to findings, Huawei and Xiaomi can be used for daily use, on the other hand it may not be appropriate for athletic performance assessments
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The Sleep Number smart bed uses embedded ballistocardiography, together with network connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing rate (BR), and sleep vs. wake states. This study evaluated the performance of the smart bed relative to polysomnography (PSG) in estimating epoch-by-epoch HR, BR, sleep vs. wake, mean overnight HR and BR, and summary sleep variables. Forty-five participants (aged 22–64 years; 55% women) slept one night on the smart bed with standard PSG. Smart bed data were compared to PSG by Bland–Altman analysis and Pearson correlation for epoch-by-epoch HR and epoch-by-epoch BR. Agreement in sleep vs. wake classification was quantified using Cohen’s kappa, ROC analysis, sensitivity, specificity, accuracy, and precision. Epoch-by-epoch HR and BR were highly correlated with PSG (HR: r = 0.81, |bias| = 0.23 beats/min; BR: r = 0.71, |bias| = 0.08 breaths/min), as were estimations of mean overnight HR and BR (HR: r = 0.94, |bias| = 0.15 beats/min; BR: r = 0.96, |bias| = 0.09 breaths/min). Calculated agreement for sleep vs. wake detection included kappa (prevalence and bias-adjusted) = 0.74 ± 0.11, AUC = 0.86, sensitivity = 0.94 ± 0.05, specificity = 0.48 ± 0.18, accuracy = 0.86 ± 0.11, and precision = 0.90 ± 0.06. For all-night summary variables, agreement was moderate to strong. Overall, the findings suggest that the Sleep Number smart bed may provide reliable metrics to unobtrusively characterize human sleep under real life-conditions.
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Objective: Examine how changes in sleep duration, objectively measured by activity trackers, impact weight gain in incoming college freshman. Participants: Incoming college freshmen, age ≥ 18. Methods: We measured weight and daily sleep duration before college entry and through the 1st college quarter. Additionally, we examined changes in sleep variability, activity levels and smartphone screen time use as possible predictors of weight gain. Results: 75 participants completed the study. Total sleep duration decreased from 437.9 ± SD 57.3 minutes at baseline to 416.5 ± SD 68.6 minutes by the end of the first quarter (p = 6.6 × 10-3). (BMI) did not change significantly in this cohort. Higher sleep variability at baseline and an increase in sleep variability were associated with increases in BMI. Smartphone screen use was note to be high (235.2 ± SD 110.3 minutes/day) at the end of the first quarter. Conclusions: College weight gain may be affected by factors other than sleep duration, including sleep variability. Supplemental data for this article can be accessed online at https://doi.org/10.1080/07448481.2022.2032720.
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Objective Sleep is fundamental for both health and wellness. The advent of “on a chip” and “smartphone” technologies have created an explosion of inexpensive, at-home applications and devices specifically addressing sleep health and sleep disordered breathing. Sleep-related smartphone Applications and devices are offering diagnosis, management, and treatment of a variety of sleep disorders, mainly obstructive sleep apnea. New technology requires both a learning curve and a review of reliability. Our objective was to evaluate which app have scientific publications as well as their potential to help in the diagnosis, management, and follow-up of sleep disordered breathing. Methods We search for relevant sleep apnea related apps on both the Google Play Store and the Apple App Store. In addition, an exhaustive literature search was carried out in MEDLINE, EMBase, web of science and Scopus for works of apps or devices that have published in the scientific literature and have been used in a clinical setting for diagnosis or treatment of sleep disordered breathing performing a systematic review. Results We found 10 smartphone apps that met the inclusion criteria. Conclusions The development of these apps and devices has a great future, but today are not as accurate as other traditional options. This new technology offers accessible, inexpensive, and continuous at home data monitoring of obstructive sleep apnea, but still does not count with proper testing and their validation may be unreliable.
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Digital phenotyping offers a powerful approach for forecasting risk for suicidal ideation or attempts over time. This method may serve as an especially useful strategy for understanding associations between sleep disorder symptoms and suicidal ideation and behavior. Sleep disorder symptoms predict suicidal ideation and behavior in cross-sectional research among active-duty military personnel, but few studies have examined longitudinal associations between sleep disorder symptoms and suicidal thoughts and behaviors in service members. In this study, we will use digital phenotyping to intensively assess Marines for 28 days through a combination of active and passive assessment strategies. Methods Marines with suicidal ideation or a suicide attempt in the past month will be recruited from Camp Lejeune, NC and provided with a Fitbit device and receive ecological momentary assessments (EMA) of suicidal urges several times throughout the day. Using dynamic multilevel models, we will explore the impact of sleep disorder symptoms on next-day suicide urges, as well as mediators of these effects. Clinical implications This study has the potential to inform optimal strategies to assess suicide risk in treatment. These findings will inform the development and implementation of real-time interventions to reduce risk for suicide among military personnel.
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Wrist skin temperature has been validated to be sensitive to the thermal state of awake people, but its correlation with thermal state of sleeping people has not been investigated. In the present study four human subject experiments were performed on both young and elderly subjects in different seasons (winter, transition, summer). Their skin temperatures were continuously measured during the whole night sleep at wrist (WST) and at the normally accepted seven points (forehead, chest, posterior forearm, hand, anterior thigh, anterior calf, and foot) proposed by Hardy & Dubois. The WST was compared with the mean skin temperature (MST) calculated with Hardy & Dubois’s seven-point method. The results show that the WST was moderately and positively correlated with MST, with the Pearson's R value ranged from 0.44 to 0.63. When comparing the variation of WST and MST throughout the whole-night sleep, the averaged maximum crossing correlation coefficient was 0.56 (Q1=0.4, Q3=0.7) and the lag time was on average -10.4 min (Q1=-11.0, Q3=0.0 min), indicating that the WST was synchronically correlated with MST. Similar to MST, the WST could differentiate sleeping micro thermal environments which were created by different combinations of indoor temperature and bedding thermal insulation. These results suggest that the WST was linearly and synchronically correlated with the MST calculated using Hardy & Dubois’s seven-point method. Thus, the skin temperature at wrist could provide information on thermal state of human body when people asleep, making it possible to measure skin temperature in field studies and to personally and energy efficiently control the thermal environment for sleep.
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Artificial intelligence (AI) combines clinical, environmental and laboratory based measures to allow a deeper understanding of sleep and sleep disorders. This article addresses various components and methods deployed in AI and covers examples of how AI is used to screen, endotype, diagnose, and treat sleep disorders. We then place this in the context of precision/personalized sleep medicine. We discuss pitfalls to ensure clinical AI implementation proceeds in the safest and most effective manner possible.
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Polysomnography (PSG) is the "gold standard" for monitoring sleep. Alternatives to PSG are of interest for clinical, research, and personal use. Wrist-worn actigraph devices have been utilized in research settings for measures of sleep for over two decades. Whether sleep measures from commercially available devices are similarly valid is unknown. We sought to determine the validity of five wearable devices: Basis Health Tracker, Misfit Shine, Fitbit Flex, Withings Pulse O2, and a research-based actigraph, Actiwatch Spectrum. We used Wilcoxon Signed Rank tests to assess differences between devices relative to PSG and correlational analysis to assess the strength of the relationship. Data loss was greatest for Fitbit and Misfit. For all devices, we found no difference and strong correlation of total sleep time with PSG. Sleep efficiency differed from PSG for Withings, Misfit, Fitbit, and Basis, while Actiwatch mean values did not differ from that of PSG. Only mean values of sleep efficiency (time asleep/time in bed) from Actiwatch correlated with PSG, yet this correlation was weak. Light sleep time differed from PSG (nREM1 + nREM2) for all devices. Measures of Deep sleep time did not differ from PSG (SWS + REM) for Basis. These results reveal the current strengths and limitations in sleep estimates produced by personal health monitoring devices and point to a need for future development.
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To validate measures of sleep and heart rate (HR) during sleep generated by a commercially-available activity tracker against those derived from polysomnography (PSG) in healthy adolescents. Sleep data were concurrently recorded using FitbitChargeHR™ and PSG, including electrocardiography (ECG), during an overnight laboratory sleep recording in 32 healthy adolescents (15 females; age, mean ± SD: 17.3 ± 2.5 years). Sleep and HR measures were compared between FitbitChargeHR™ and PSG using paired t-tests and Bland-Altman plots. Epoch-by-epoch analysis showed that FitbitChargeHR™ had high overall accuracy (91%), high sensitivity (97%) in detecting sleep, and poor specificity (42%) in detecting wake on a min-to-min basis. On average, FitbitChargeHR™ significantly but negligibly overestimated total sleep time by 8 min and sleep efficiency by 1.8%, and underestimated wake after sleep onset by 5.6 min (p < 0.05). Within FitbitChargeHR™ epochs of sleep, the average HR was 59.3 ± 7.5 bpm, which was significantly but negligibly lower than that calculated from ECG (60.2 ± 7.6 bpm, p < 0.001), with no change in mean discrepancies throughout the night. FitbitChargeHR™ showed good agreement with PSG and ECG in measuring sleep and HR during sleep, supporting its use in assessing sleep and cardiac function in healthy adolescents. Further validation is needed to assess its reliability over prolonged periods of time in ecological settings and in clinical populations.
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Lukasz Piwek and colleagues consider whether wearable technology can become a valuable asset for health care.
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A 36-item short-form (SF-36) was constructed to survey health status in the Medical Outcomes Study. The SF-36 was designed for use in clinical practice and research, health policy evaluations, and general population surveys. The SF-36 includes one multi-item scale that assesses eight health concepts: 1) limitations in physical activities because of health problems; 2) limitations in social activities because of physical or emotional problems; 3) limitations in usual role activities because of physical health problems; 4) bodily pain; 5) general mental health (psychological distress and well-being); 6) limitations in usual role activities because of emotional problems; 7) vitality (energy and fatigue); and 8) general health perceptions. The survey was constructed for self-administration by persons 14 years of age and older, and for administration by a trained interviewer in person or by telephone. The history of the development of the SF-36, the origin of specific items, and the logic underlying their selection are summarized. The content and features of the SF-36 are compared with the 20-item Medical Outcomes Study short-form.
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Actigraphy for long-term sleep/wake monitoring fails to correctly classify situations where the subject displays low activity, but is awake. In this paper we propose a new algorithm which uses both accelerometer and cardio-respiratory signals to overcome this restriction. Acceleration, electrocardiogram and respiratory effort were measured with an integrated wearable recording system worn on the chest by three healthy male subjects during normal daily activities. For signal processing a Fast Fourier Transformation and as classifier a feed-forward Artificial Neural Network was used. The best classifier achieved an accuracy of 96.14%, a sensitivity of 94.65% and a specificity of 98.19%. The algorithm is suitable for integration into a wearable device for long-term home monitoring.
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The durations of successive sleep cycles, defined according to NREM (stage 2) or REM onsets, were objected to trend analysis in three groups of normal subjects and in a group of elderly patients with chronic brain syndrome (CBS). NREM sleep cycles showed consistent curvilinear trends for all groups except that the trend in children was distinguished by a lengthy first NREM cycle. REM steep cycles showed quite similar curvilinear trends for the three normal age groups with the middle two cycles being longer than the first and fourth. In the CBS patients, REM sleep cycles did not show a significant trend across the night. Real-time cycles (i.e., with time awake included) manifested trends quite similar to those excluding waking. The trends in sleep cycle durations are normative characteristics of sleep which may not be apparent on a single night. A more constant cycle was found in the CBS elderly and may indicate brain pathology. Sleep cycle trends, along with such other temporal characteristics as the decline in stage 4, may provide clues to the metabolic processes which underlie the sleep EEG. They also provide a more exact basis for investigation of hypothesized biorhythm correlates of NREM-REM cycles.
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A 36-item short-form (SF-36) was constructed to survey health status in the Medical Outcomes Study. The SF-36 was designed for use in clinical practice and research, health policy evaluations, and general population surveys. The SF-36 includes one multi-item scale that assesses eight health concepts: 1) limitations in physical activities because of health problems; 2) limitations in social activities because of physical or emotional problems; 3) limitations in usual role activities because of physical health problems; 4) bodily pain; 5) general mental health (psychological distress and well-being); 6) limitations in usual role activities because of emotional problems; 7) vitality (energy and fatigue); and 8) general health perceptions. The survey was constructed for self-administration by persons 14 years of age and older, and for administration by a trained interviewer in person or by telephone. The history of the development of the SF-36, the origin of specific items, and the logic underlying their selection are summarized. The content and features of the SF-36 are compared with the 20-item Medical Outcomes Study short-form.
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The use of an efficient noninvasive method to investigate the autonomic nervous system and cardiovascular control during sleep. Beat-to-beat heart rate variability displays two main components: a low-frequency (LF) one representing sympathetic and parasympathetic influence and a high-frequency (HF) component of parasympathetic origin. Sympathovagal balance can be defined as LF/HF ratio. We reviewed normal, standardly staged all-night polysomnograms from 10 healthy children aged 6 to 17 years. Recorded 256-second traces of heart rate and respiration were sampled. Power spectra of instantaneous heart rate and respiration were computed using a fast Fourier transform method. The study revealed a decrease in LF during sleep, with minimal values during non-REM slow-wave sleep and elevated levels similar to those of wakefulness during REM. HF increased with sleep onset, reaching maximal values during slow-wave sleep, and behaved as a mirror image of LF. LF/HF ratio displayed changes similar to those in LF. The sympathetic predominance that characterizes wakefulness decreases during non-REM sleep, is minimal in slow-wave sleep, and surges toward mean awake levels during REM sleep. The autonomic balance is shifted toward parasympathetic predominance during slow-wave sleep. This noninvasive method used to outline autonomic activity achieves results that are in complete agreement with those obtained with direct invasive tools.
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Background: Insomnia is a prevalent health complaint that is often difficult to evaluate reliably. There is an important need for brief and valid assessment tools to assist practitioners in the clinical evaluation of insomnia complaints.Objective: This paper reports on the clinical validation of the Insomnia Severity Index (ISI) as a brief screening measure of insomnia and as an outcome measure in treatment research. The psychometric properties (internal consistency, concurrent validity, factor structure) of the ISI were evaluated in two samples of insomnia patients.Methods: The first study examined the internal consistency and concurrent validity of the ISI in 145 patients evaluated for insomnia at a sleep disorders clinic. Data from the ISI were compared to those of a sleep diary measure. In the second study, the concurrent validity of the ISI was evaluated in a sample of 78 older patients who participated in a randomized-controlled trial of behavioral and pharmacological therapies for insomnia. Change scores on the ISI over time were compared with those obtained from sleep diaries and polysomnography. Comparisons were also made between ISI scores obtained from patients, significant others, and clinicians.Results: The results of Study 1 showed that the ISI has adequate internal consistency and is a reliable self-report measure to evaluate perceived sleep difficulties. The results from Study 2 also indicated that the ISI is a valid and sensitive measure to detect changes in perceived sleep difficulties with treatment. In addition, there is a close convergence between scores obtained from the ISI patient's version and those from the clinician's and significant other's versions.Conclusions: The present findings indicate that the ISI is a reliable and valid instrument to quantify perceived insomnia severity. The ISI is likely to be a clinically useful tool as a screening device or as an outcome measure in insomnia treatment research.
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The intent of the study was to explore the nature and function of the cardiovascular activation response that occurs at an arousal from sleep. Four experiments were conducted. The first compared the pattern of physiologic response to orienting and startle stimuli and arousal from sleep. The second and third measured the amplitude of the cardiovascular arousal response as a function of the trait of fearfulness and the threat value of the arousing stimulus, respectively. The final experiment assessed the effect of arousal duration. The experiments were conducted in the sleep laboratory of the Department of Psychology, University of Melbourne. A total of 42 (24 women and 18 men) healthy individuals between the ages of 18 and 24 participated in the experiments. The experiments manipulated the stimuli to which participants were exposed (orienting and startle stimuli and arousal from sleep), the threat value of stimuli used to arouse participants from sleep, and individual differences in fearfulness. The major dependent variables were heart rate, blood pressure, and a measure of peripheral vasoconstriction (digital pulse volume). In addition, in the first study, the galvanic skin response and orbicularis oculi electromyographic activity were measured. Experiment 1 showed that the pattern of physiologic response at an arousal from sleep differed, with a substantially larger cardiovascular component, from responses to orienting and startle stimuli. Experiments 2a and 2b indicated that the magnitude of the cardiovascular response at an arousal was unrelated to either individual differences in fearfulness or differences in the threat value of arousing stimuli. The final experiment showed that the cardiovascular response at an arousal was not a return to waking levels of activity but, rather, was a transient activation response. The study supported the view that the cardiovascular activation response at an arousal from sleep is a transient, reflex-like response that is different from the response that occurs during normal wakefulness.
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To evaluate the ability of actigraphy compared to polysomnography (PSG) to detect wakefulness in subjects submitted to 3 sleep conditions with different amounts of wakefulness: a nocturnal sleep episode and 2 daytime recovery sleep episodes, one with placebo and one with caffeine. A second objective was to compare the ability of 4 different scoring algorithms (2 threshold algorithms and 2 regression analysis algorithms) to detect wake in the 3 sleep conditions. Three nights of simultaneous actigraphy (Actiwatch-L, Mini-Mitter/Respironics) and PSG recordings in a within-subject design. Chronobiology laboratory. Fifteen healthy subjects aged between 20 and 60 years (7M, 8F). 200 mg of caffeine and daytime recovery sleep. An epoch-by-epoch comparison between actigraphy and PSG showed a significant decrease in actigraphy accuracy with increased wakefulness in sleep conditions due to the low sleep specificity of actigraphy (generally <50%). Actigraphy overestimated total sleep time and sleep efficiency more strongly in conditions involving more wakefulness. Compared to the 2 regression algorithms, the 2 threshold algorithms were less able to detect wake when the sleep episode involved more wakefulness, and they tended to alternate more between wake and sleep in the scoring of long periods of wakefulness resulting in an overestimation of the number of awakenings. The very low ability of actigraphy to detect wakefulness casts doubt on its validity to measure sleep quality in clinical populations with fragmented sleep or in situations where the sleep-wake cycle is challenged, such as jet lag and shift work.
Article
The concurrent usage of actigraphy and heart rate variability (HRV) for sleep efficiency quantification is still matter of investigation. This study compared chest (CACT) and wrist (WACT) actigraphy (actigraphs positioned on chest and wrist, respectively) in combination with HRV for automatic sleep vs wake classification. Accelerometer and ECG signals were collected during polysomnographic studies (PSGs) including 18 individuals (25–53 years old) with no previous history of sleep disorders. Then, an experienced neurologist performed sleep staging on PSG data. Eleven features from HRV and accelerometry were extracted from series of different lengths. A support vector machine (SVM) was used to automatically distinguish sleep and wake. We found 7 min as the optimal signal length for classification, while maximizing specificity (wake detection). CACT and WACT provided similar accuracies (78% chest vs 77% wrist), larger than what yielded by HRV alone (66%). The addition of HRV to CACT reduced slightly the accuracy, while improving specificity (from 33% to 51%, p < 0.05). On the contrary, the concurrent usage of HRV and WACT did not provide statistically significant improvements over WACT. Then, a subset of features (3 from HRV + 1 from actigraphy) was selected by reducing redundancy using a strategy based on Spearman's correlation and area under the ROC curve. The usage of the reduced set of features and SVM classifier gave only slightly reduced classification performances, which did not differ from the full sets of features. The study opens interesting possibilities in the design of wearable devices for long-term monitoring of sleep at home.
Article
Introduction We investigated the ability of a wrist-worn tracker to estimate sleep stages in normal adult sleepers. Such a device could be useful in simplifying sleep research and in increasing public knowledge of sleep. Methods Movement and cardiac data was collected from 60 adult subjects (36 M: 24 F, ages 34 ± 10 yrs) wearing two wrist worn devices (left and right hand) containing a 3D-accelerometer and an optical photoplethysmogram (PPG), while undergoing a sleep stage study using a Type III home sleep testing device. The accelerometer was used to generate various features of movement; the PPG records cardiac peaks generated by each heartbeat, and can be used to determine heart rate and heart rate variability metrics. The sleep study was scored independently by two registered PSG technicians, using consensus AASM scoring rules. Using these labels, an automated classifier and post-processing rule was developed to label 30-second epochs as one of Wake/Light/Deep/REM (note that Stages N1 and N2 were combined into a single “Light” classification). The estimated performance of this automated classifier system was calculated using a leave-one out validation method. The performance metrics were Cohen’s kappa (measures the level of agreement greater than chance) and per-epoch accuracy (percent of epochs correctly labeled). Results The estimated Cohen’s kappa was 0.52 ± 0.14 for left hand wear, and 0.53 ± 0.14 (right hand). The per-epoch accuracy was 69%. Across the population, there was no statistically significant bias in the estimated durations of the wake, light, deep and REM stages versus the gold standard measurements. Conclusion These results suggest that a wrist worn device with movement and cardiac sensors can be used to determine sleep stages with a reasonable degree of accuracy in normal adult sleepers, but without the cost and artificial sleep environment of a sleep laboratory. The reported performance figures are similar or better than previously reported results from non-EEG based sleep staging using combinations of cardiac, respiratory and movement information. Support (If Any) This work was supported by Fitbit.
Article
Background Sleep disturbance is a common and important component of affective illness. Fitness activity trackers are emerging as alternative means to estimate sleep in psychiatric patients; however, their ability to quantify sleep in mood disorders has not been empirically evaluated. Thus, this study sought to evaluate the utility of the Fitbit Flex (FBF) to estimate sleep in patients with major depressive disorder (MDD) relative to gold standard polysomnography (PSG) and a widely-used actigraph (Actiwatch-2; AW-2). Methods Twenty-one patients with unipolar MDD wore the FBF and AW-2 during in-laboratory PSG. Bland-Altman analysis compared sleep variables among devices. Epoch-by-epoch analysis further evaluated sensitivity, specificity, and accuracy for the FBF and AW-2 relative to PSG. Results The FBF demonstrated significant limitations in quantifying sleep and wake, relative to PSG. In the normal setting, the FBF significantly overestimated sleep time and efficiency, and displayed poor ability to correctly identify wake epochs (i.e. low specificity). In the sensitive setting, the FBF significantly underestimated sleep time and efficiency relative to PSG. Performance characteristics of the FBF were more similar to the AW-2 in the normal compared to sensitive setting. Limitations Participants were young to middle aged and predominantly female, which may limit generalizability of findings. Study design also precluded ability to assess longitudinal performance of FBF. Conclusions The FBF is not an adequate substitute for PSG when quantifying sleep in MDD, and the settings of the device sizably impact its performance relative to PSG and other standard actigraphs. The limitations and capabilities of the FBF should be carefully considered prior to clinical and research implementation.
Article
Objectives: To compare the accuracy of the commercial Fitbit Flex device (FF) with polysomnography (PSG; the gold-standard method) in insomnia disorder patients and good sleepers. Methods: Participants wore an FF and actigraph while undergoing overnight PSG. Primary outcomes were intraclass correlation coefficients (ICCs) of the total sleep time (TST) and sleep efficiency (SE), and the frequency of clinically acceptable agreement between the FF in normal mode (FFN) and PSG. The sensitivity, specificity, and accuracy of detecting sleep epochs were compared among FFN, actigraphy, and PSG. Results: The ICCs of the TST between FFN and PSG in the insomnia (ICC=0.886) and good-sleepers (ICC=0.974) groups were excellent, but the ICC of SE was only fair in both groups. The TST and SE were overestimated for FFN by 6.5min and 1.75%, respectively, in good sleepers, and by 32.9min and 7.9% in the insomnia group with respect to PSG. The frequency of acceptable agreement of FFN and PSG was significantly lower (p=0.006) for the insomnia group (39.4%) than for the good-sleepers group (82.4%). The sensitivity and accuracy of FFN in an epoch-by-epoch comparison with PSG was good and comparable to those of actigraphy, but the specificity was poor in both groups. Conclusions: The ICC of TST in the FFN-PSG comparison was excellent in both groups, and the frequency of agreement was high in good sleepers but significantly lower in insomnia patients. These limitations need to be considered when applying commercial sleep trackers for clinical and research purposes in insomnia.
Article
Objective/background: To evaluate the performance of a multisensor sleep-tracker (ŌURA ring) against polysomnography (PSG) in measuring sleep and sleep stages. Participants: Forty-one healthy adolescents and young adults (13 females; Age: 17.2 ± 2.4 years). Methods: Sleep data were recorded using the ŌURA ring and standard PSG on a single laboratory overnight. Metrics were compared using Bland-Altman plots and epoch-by-epoch (EBE) analysis. Results: Summary variables for sleep onset latency (SOL), total sleep time (TST), and wake after sleep onset (WASO) were not different between ŌURA ring and PSG. PSG-ŌURA discrepancies for WASO were greater in participants with more PSG-defined WASO (p < .001). Compared with PSG, ŌURA ring underestimated PSG N3 (~20 min) and overestimated PSG REM (~17 min; p < .05). PSG-ŌURA differences for TST and WASO lay within the ≤ 30 min a-priori-set clinically satisfactory ranges for 87.8% and 85.4% of the sample, respectively. From EBE analysis, ŌURA ring had a 96% sensitivity to detect sleep, and agreement of 65%, 51%, and 61%, in detecting "light sleep" (N1), "deep sleep" (N2 + N3), and REM sleep, respectively. Specificity in detecting wake was 48%. Similarly to PSG-N3 (p < .001), "deep sleep" detected with the ŌURA ring was negatively correlated with advancing age (p = .001). ŌURA ring correctly categorized 90.9%, 81.3%, and 92.9% into PSG-defined TST ranges of < 6 hr, 6-7 hr, > 7 hr, respectively. Conclusions: Multisensor sleep trackers, such as the ŌURA ring have the potential for detecting outcomes beyond binary sleep-wake using sources of information in addition to motion. While these first results could be viewed as promising, future development and validation are needed.
Article
Biological needs for sleep are met by engaging in behaviors that are largely influenced by the environment, social norms and demands, and societal influences and pressures. Insufficient sleep duration and sleep disorders such as insomnia and sleep apnea are highly prevalent in the US population. This article outlines some of these downstream factors, including cardiovascular and metabolic disease risk, neurocognitive dysfunction, and mortality, as well as societal factors such as age, sex, race/ethnicity, and socioeconomics. This review also discusses societal factors related to sleep, such as globalization, health disparities, public policy, public safety, and changing patterns of use of technology.
Article
Consumer sleep tracking devices such as fitness trackers and smartphone apps have become increasingly popular. These devices claim to measure the sleep duration of their users and in some cases purport to measure sleep quality and awaken users from light sleep, potentially improving overall sleep. Most of these devices appear to utilize data generated from in-built accelerometers to determine sleep parameters but the exact mechanisms and algorithms are proprietary. The growing literature comparing these devices against polysomnography/ actigraphy shows that they tend to underestimate sleep disruptions and overestimate total sleep times and sleep efficiency in normal subjects. In this review, we evaluate the current literature comparing the accuracy of consumer sleep tracking devices against more conventional methods used to measure sleep duration and quality. We discuss the current technology that these devices utilize as well as summarize the value of these devices in clinical evaluations and their potential limitations.
Article
To investigate other physiologic changes that occur with periodic leg movements during sleep (PLMS) that might be considered to be more sensitive indices of sleep fragmentation. Although PLMS are associated with recurrent microarousals (MA), the frequency of PLMS with MA does not correlate with objective daytime sleepiness. It is postulated that the lack of correlation results from the low sensitivity of the standard criteria used to score MA. Ten drug-free patients with a polygraphic and clinical diagnosis of restless legs syndrome (RLS) and PLMS were examined. The EEG correlates of PLMS were analyzed by visual scoring and spectral analysis during PLMS that ended in a visible microarousal (PLMS with MA) or not (PLMS without MA). The R-R interval in the EKG signal was also examined. A total of 34% of PLMS were associated with MA lasting >3 seconds, and 3% of PLMS were associated with MA lasting <3 seconds. Although PLMS with MA were associated with an increase in alpha activity, for PLMS without MA a significant increase in delta and theta activity was present. Both types of PLMS induced a shortening of the R-R interval; this was particularly more marked for PLMS with MA. First, visual scoring of MA that include a duration of less than 3 seconds has little effect on the detection of PLMS with MA. Second, EEG activation and tachycardia are present during both types of PLMS. Third, a hierarchy in the arousal response is present-going from autonomic activation to bursts of delta activity to alpha activity to a full awakening.
Book
This authoritative guide to sleep medicine is also available as an e-dition, book (ISBN: 1416003207) plus updated online reference! The new edition of this definitive resource has been completely revised and updated to provide all of the latest scientific and clinical advances. Drs. Kryger, Roth, and Dementand over 170 international expertsdiscuss the most recent data, management guidelines, and treatments for a full range of sleep problems. Representing a wide variety of specialties, including pulmonary, neurology, psychiatry, cardiology, internal medicine, otolaryngology, and primary care, this whos who of experts delivers the most compelling, readable, and scientifically accurate source of sleep medicine available today. Includes user-friendly synopses of important background information before all basic science chapters. Provides expert coverage of narcolepsy * movement disorders * breathing disorders * gastrointestinal problems * neurological conditions * psychiatric disturbances * substance abuse * and more. Discusses hot topics such as the genetic mechanisms of circadian rhythms * the relationship between obesity, hormones, and sleep apnea * sleep apnea and arterial hypertension * and more. Includes a new section on Cardiovascular Disorders that examines the links between sleep breathing disorders and cardiovascular abnormalities, as well as the use of sleep related therapies for congestive heart failure. Provides a new section on Womens Health and Sleep Disorders that includes information on the effects of hormonal changes during pregnancy and menopause on sleep. Features the fresh perspectives of 4 new section editors. Employs a more consistent chapter organization for better readability and easier navigation.
Article
Study objectives: To investigate the relationship between K-complexes (KCs) and cardiac functioning. Methods: Forty healthy adolescents age 16-22 y (19 females) participated in the study. Heart rate (HR) fluctuations associated with spontaneous and evoked KCs were investigated on two nights, one with (event-related potential night) and one without auditory tones presented across the night. Results: There was a clear biphasic cardiac response to evoked and spontaneous KCs, with an initial acceleration in HR followed by a deceleration (P < 0.001). It occurred immediately to KCs in response to tones presented in the first third of the interbeat interval, but was delayed a beat when the tone occurred later in the cardiac cycle (P < 0.05). Sex differences were also evident. Pretone baseline HR was higher, and the magnitude of the HR response was blunted and delayed, in females compared to males (P < 0.001). Also, pretone baseline HR was lower when a tone elicited a KC compared to when it did not (P < 0.001), suggesting that KCs are possibly more likely to be elicited by external stimuli in states of reduced cardiac activation. Conclusions: The strict dependency observed between KCs and cardiac control indicates a potential role of KCs in modulating the cardiovascular system during sleep. Sex differences in the KC-cardiac response indicate the sensitivity of this measure in capturing sex differences in cardiac regulatory physiology.
Article
Objective: Periodic limb movements during sleep (PLMS) are sleep phenomena characterized by periodic episodes of repetitive stereotyped limb movements. The aim of this study was to describe the prevalence and determinants of PLMS in a middle-to-older age general population. Methods: Data from 2162 subjects (51.2% women, mean age 58.4±11.1 years) participating in a population-based study (HypnoLaus, Lausanne, Switzerland) were collected. Assessments included laboratory, socio-demographic, personal and treatment history, and full polysomnography at home. PLMS index (PLMSI) was determined and a PLMSI>15/h was considered as significant. Results: Prevalence of PLMSI>15/h was 28.6% (31.3% in men, 26% in women). Compared to subjects with a PLMSI≤15/h, subjects with a PLMSI>15/h were older (p<0.001), predominantly males (p=0.007), with a higher proportion of restless legs syndrome (RLS, p<0.001), a higher BMI (p=0.001), and a lower mean glomerular filtration rate (p<0.001). Subjects with a PLMSI>15/h also had a higher prevalence of diabetes, hypertension, and betablocker or hypnotic treatments. The prevalence of antidepressant use was higher, but not statistically significant (p=0.07). Single nucleotide polymorphisms (SNP) within BTBD9 (rs3923809), TOX3 (rs3104788) and MEIS1 (rs2300478) genes were significantly associated with PLSMI>15/h. Conversely, mean hemoglobin and ferritin levels were similar in both groups. In the multivariate analysis, age, male gender, antidepressant intake, RLS and rs3923809, rs3104788 and rs2300478 SNPs were independently associated with a PLMSI>15/h. Interpretation: PLMS are highly prevalent in our middle-age European population. Age, male gender, RLS, antidepressant treatment and specific BTBD9, TOX3 and MEIS1 SNPs distribution are independent predictors of a PLMSI >15/h. This article is protected by copyright. All rights reserved.
Article
Objective: To compare two commercial sleep devices, an accelerometer worn as a wristband (UP) and a smartphone application (MotionX 24/7), against polysomnography (PSG) and actigraphy (Actiwatch2) in a clinical pediatric sample. Patients and methods: Children and adolescents (N = 78, 65% male, mean age 8.4±4.0 y) with suspected sleep disordered breathing (SDB), simultaneously wore an actiwatch, a commercial wrist-based device and had a smartphone with a sleep application activated placed near their right shoulder, during their diagnostic PSG. Outcome variables were sleep onset latency (SOL), total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency (SE). Paired comparisons were made between PSG, actigraphy, UP, and MotionX 24/7. Epoch-by-epoch comparisons determined sensitivity, specificity, and accuracy between PSG, actigraphy, and UP. Bland-Altman plots determined level of agreement. Differences in bias between SDB severity and developmental age were assessed. Results: No differences in mean TST, WASO, or SE between PSG and actigraphy or PSG and UP were found. Actigraphy overestimated SOL (21 min). MotionX 24/7 underestimated SOL (12 min) and WASO (63 min), and overestimated TST (106 min) and SE (17%). UP showed good sensitivity (0.92) and accuracy (0.86) but poor specificity (0.66) when compared to PSG. Bland-Altman plots showed similar levels of bias in both actigraphy and UP. Bias did not differ by SDB severity, however was affected by age. Conclusions: When compared to PSG, UP was analogous to Actiwatch2 and may have some clinical utility in children with sleep disordered breathing. MotionX 24/7 did not accurately reflect sleep or wake and should be used with caution.
Article
STUDY OBJECTIVES: To evaluate the accuracy in measuring nighttime sleep of a fitness tracker (Jawbone UP) compared to polysomnography (PSG). DESIGN: Jawbone UP and PSG data were simultaneously collected from adolescents during an overnight laboratory recording. Agreements between Jawbone UP and PSG sleep outcomes were analyzed using paired t tests and Bland-Altman plots. Multiple regressions were used to investigate which PSG sleep measures predicted Jawbone UP "Sound sleep" and "Light sleep". SETTING: SRI International Human Sleep Laboratory. PARTICIPANTS: Sixty-five healthy adolescents (28 females, mean age ± standard deviation [SD]: 15.8 ± 2.5 y). INTERVENTIONS: N/A. MEASUREMENTS AND RESULTS: Outcomes showed good agreements between Jawbone UP and PSG for total sleep time (mean differences ± SD: -10.0 ± 20.5 min), sleep efficiency (mean differences ± SD: -1.9 ± 4.2 %), and wake after sleep onset (WASO) (mean differences ± SD: 10.6 ± 14.7 min). Overall, Jawbone UP overestimated PSG total sleep time and sleep efficiency and underestimated WASO but differences were small and, on average, did not exceed clinically meaningful cutoffs of > 30 min for total sleep time and > 5% for sleep efficiency. Multiple regression models showed that Jawbone UP "Sound sleep" measure was predicted by PSG time in N2 (β = 0.25), time in rapid eye movement (β = 0.29), and arousal index (β = -0.34). Jawbone UP "Light sleep" measure was predicted by PSG time in N2 (β = 0.48), time in N3 (β = 0.49), arousal index (β = 0.38) and awakening index (β = 0.28). Jawbone UP showed a progression from slight overestimation to underestimation of total sleep time and sleep efficiency with advancing age. All relationships were similar in boys and girls. CONCLUSIONS: Jawbone UP shows good agreement with PSG in measures of total sleep time and WASO in adolescent boys and girls. Further validation is needed in other age groups and clinical populations before advocating use of these inexpensive and easy-to-use devices in clinical sleep medicine and research.
Article
Given the recognition that sleep may influence obesity risk, there is increasing interest in measuring sleep parameters within obesity studies. The goal of the current analyses was to determine whether the SenseWear(®) Pro3 Armband (armband), typically used to assess physical activity, is reliable at assessing sleep parameters. The armband was compared with the AMI Motionlogger(®) (actigraph), a validated activity monitor for sleep assessment, and with polysomnography, the gold standard for assessing sleep. Participants were 20 adolescents (mean age = 15.5 years) with a mean body mass index percentile of 63.7. All participants wore the armband and actigraph on their non-dominant arm while in-lab during a nocturnal polysomnographic recording (600 min). Epoch-by-epoch sleep/wake data and concordance of sleep parameters were examined. No significant sleep parameter differences were found between the armband and polysomnography; the actigraph tended to overestimate sleep and underestimate wake compared with polysomnography. Both devices showed high sleep sensitivity, but lower wake detection rates. Bland-Altman plots showed large individual differences in armband sleep parameter concordance rates. The armband did well estimating sleep overall, with group results more similar to polysomnography than the actigraph; however, the armband was less accurate at an individual level than the actigraph. © 2015 European Sleep Research Society.
Article
To evaluate the reliability and validity of the commercially available Fitbit accelerometer compared to polysomnography (PSG) and two different actigraphs in a pediatric sample. All subjects wore the Fitbit while undergoing overnight clinical polysomnography in a sleep laboratory; a randomly selected subset of participants also wore either the Ambulatory Monitoring Inc. Motionlogger Sleep Watch (AMI) or Phillips-Respironics Mini-Mitter Spectrum (PRMM). 63 youth (32 females, 31 males), ages 3-17 years (mean 9.7 years, SD 4.6 years). Both "Normal" and "Sensitive" sleep-recording Fitbit modes were examined. Outcome variables included total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency (SE). Primary analyses examined the differences between Fitbit and PSG using repeated-measures ANCOVA, with epoch-by-epoch comparisons between Fitbit and PSG used to determine sensitivity, specificity, and accuracy. Intra-device reliability, differences between Fitbit and actigraphy, and differences by both developmental age group and sleep disordered breathing (SDB) status were also examined. Compared to PSG, the Normal Fitbit mode demonstrated good sensitivity (0.86) and accuracy (0.84), but poor specificity (0.52); conversely, the Sensitive Fitbit mode demonstrated adequate specificity (0.79), but inadequate sensitivity (0.70) and accuracy (0.71). Compared to PSG, the Fitbit significantly overestimated TST (41 min) and SE (8%) in Normal mode, and underestimated TST (105 min) and SE (21%) in Sensitive mode. Similar differences were found between Fitbit (both modes) and both brands of actigraphs. Despite its low cost and ease of use for consumers, neither sleep-recording mode of the Fitbit accelerometer provided clinically comparable results to PSG. Further, pediatric sleep researchers and clinicians should be cautious about substituting these devices for validated actigraphs, with a significant risk of either overestimating or underestimating outcome data including total sleep time and sleep efficiency. Copyright © 2015 Associated Professional Sleep Societies, LLC. All rights reserved.
Article
Although polysomnography is necessary for diagnosis of most sleep disorders, it is also expensive, time-consuming, intrusive, and interferes with sleep. Field-based activity monitoring is increasingly used as an alternative measure that can be used to answer certain clinical and research questions. The purpose of this study was to evaluate the reliability and validity of a novel activity monitoring device (Fitbit) compared to both polysomnography and standard actigraphy (Actiwatch-64). To test validity, simultaneous Fitbit and actigraph were worn during standard overnight polysomnography by 24 healthy adults at the West Virginia University sleep research laboratory. To test inter-Fitbit reliability, three participants also wore two of the Fitbit devices overnight at home. Fitbit showed high intradevice reliability = 96.5-99.1. Fitbit and actigraph differed significantly on recorded total sleep time and sleep efficiency between each other and polysomnography. Bland-Altman plots indicated that both Fitbit and actigraph overestimated sleep efficiency and total sleep time. Sensitivity of both Fitbit and actigraphy for accurately identifying sleep was high within all sleep stages and during arousals; specificity of both Fitbit and actigraph for accurately identifying wake was poor. Specificity of actigraph was higher except for wake before sleep onset; sensitivity of Fitbit was higher in all sleep stages and during arousals. The web-based Fitbit, available at a markedly reduced price and with several convenience factors compared to standard actigraphy, may be an acceptable activity measurement instrument for use with normative populations. However, Fitbit has the same specificity limitations as actigraphy; both devices consistently misidentify wake as sleep and thus overestimate both sleep time and quality. Use of the Fitbit will also require specific validation before it can be used to assess disordered populations and or different age groups.
Article
We investigated the potential of adding cardiac and respiratory activity information to actigraphy for sleep-wake staging. A dataset of 35 recordings with full polysomnography and actigraphy was used to assess the performance of an automated sleep/wake Bayesian classifier using electrocardiogram, inductance plethysmogram estimate of respiratory effort and actigraphy. The best performance was achieved with the linear discriminant model that provided an agreement of Cohen's kappa=0.62 for one of the configurations of the classifier, corresponding to an accuracy of 86.8%, a sensitivity of 66.9% and a specificity of 93.1%. It shows that combining different vital signs for a home sleep-wake staging system could be a promising approach.